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Sample records for survival analysis model

  1. Survival analysis models and applications

    CERN Document Server

    Liu, Xian

    2012-01-01

    Survival analysis concerns sequential occurrences of events governed by probabilistic laws.  Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis.Assumes only a minimal knowledge of SAS whilst enablin

  2. Frailty Models in Survival Analysis

    CERN Document Server

    Wienke, Andreas

    2010-01-01

    The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. "Frailty Models in Survival Analysis" presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of

  3. Model selection criterion in survival analysis

    Science.gov (United States)

    Karabey, Uǧur; Tutkun, Nihal Ata

    2017-07-01

    Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

  4. Statistical models and methods for reliability and survival analysis

    CERN Document Server

    Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

    2013-01-01

    Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

  5. Survival Analysis

    CERN Document Server

    Miller, Rupert G

    2011-01-01

    A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

  6. Mediation analysis for survival data using semiparametric probit models.

    Science.gov (United States)

    Huang, Yen-Tsung; Cai, Tianxi

    2016-06-01

    Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through mediators. Currently, the literature on mediation analyses with survival outcomes largely focused on settings with a single mediator and quantified the mediation effects on the hazard, log hazard and log survival time (Lange and Hansen 2011; VanderWeele 2011). In this article, we propose a multi-mediator model for survival data by employing a flexible semiparametric probit model. We characterize path-specific effects (PSEs) of the exposure on the outcome mediated through specific mediators. We derive closed form expressions for PSEs on a transformed survival time and the survival probabilities. Statistical inference on the PSEs is developed using a nonparametric maximum likelihood estimator under the semiparametric probit model and the functional Delta method. Results from simulation studies suggest that our proposed methods perform well in finite sample. We illustrate the utility of our method in a genomic study of glioblastoma multiforme survival. © 2015, The International Biometric Society.

  7. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  8. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  9. Modelling survival

    DEFF Research Database (Denmark)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight

    2016-01-01

    well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates...

  10. Flexible survival regression modelling

    DEFF Research Database (Denmark)

    Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben

    2009-01-01

    Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...

  11. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    Science.gov (United States)

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

  12. Parametric and semiparametric models with applications to reliability, survival analysis, and quality of life

    CERN Document Server

    Nikulin, M; Mesbah, M; Limnios, N

    2004-01-01

    Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

  13. Up-to-date and precise estimates of cancer patient survival: model-based period analysis.

    Science.gov (United States)

    Brenner, Hermann; Hakulinen, Timo

    2006-10-01

    Monitoring of progress in cancer patient survival by cancer registries should be as up-to-date as possible. Period analysis has been shown to provide more up-to-date survival estimates than do traditional methods of survival analysis. However, there is a trade-off between up-to-dateness and the precision of period estimates, in that increasing the up-to-dateness of survival estimates by restricting the analysis to a relatively short, recent time period, such as the most recent calendar year for which cancer registry data are available, goes along with a loss of precision. The authors propose a model-based approach to maximize the up-to-dateness of period estimates at minimal loss of precision. The approach is illustrated for monitoring of 5-year relative survival of patients diagnosed with one of 20 common forms of cancer in Finland between 1953 and 2002 by use of data from the nationwide Finnish Cancer Registry. It is shown that the model-based approach provides survival estimates that are as up-to-date as the most up-to-date conventional period estimates and at the same time much more precise than the latter. The modeling approach may further enhance the use of period analysis for deriving up-to-date cancer survival rates.

  14. Applied survival analysis using R

    CERN Document Server

    Moore, Dirk F

    2016-01-01

    Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics...

  15. Evaluation of parametric models by the prediction error in colorectal cancer survival analysis.

    Science.gov (United States)

    Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Orooji, Arezoo; Pourhoseingholi, Mohamad Amin; Zali, Mohammad Reza

    2015-01-01

    The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis.

  16. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    Science.gov (United States)

    Asher, Lucy; Harvey, Naomi D.; Green, Martin; England, Gary C. W.

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided. PMID:28804710

  17. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs.

    Science.gov (United States)

    Asher, Lucy; Harvey, Naomi D; Green, Martin; England, Gary C W

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided.

  18. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    Directory of Open Access Journals (Sweden)

    Lucy Asher

    2017-07-01

    Full Text Available Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between a binary event(s and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses, and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided.

  19. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    OpenAIRE

    Lucy Asher; Harvey, Naomi D.; Martin Green; England, Gary C.W.

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that...

  20. Effects of temperature on development, survival and reproduction of insects: experimental design, data analysis and modeling.

    Science.gov (United States)

    Régnière, Jacques; Powell, James; Bentz, Barbara; Nealis, Vincent

    2012-05-01

    The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to temperature poses practical challenges because of their modality, variability among individuals and high mortality near the lower and upper threshold temperatures. We address this challenge with an integrated approach to the design of experiments and analysis of data based on maximum likelihood. This approach expands, simplifies and unifies the analysis of laboratory data parameterizing the thermal responses of insects in particular and poikilotherms in general. This approach allows the use of censored observations (records of surviving individuals that have not completed development after a certain time) and accommodates observations from temperature transfer treatments in which individuals pass only a portion of their development at an extreme (near-threshold) temperature and are then placed in optimal conditions to complete their development with a higher rate of survival. Results obtained from this approach are directly applicable to individual-based modeling of insect development, survival and reproduction with respect to temperature. This approach makes possible the development of process-based phenology models that are based on optimal use of available information, and will aid in the development of powerful tools for analyzing eruptive insect population behavior and response to changing climatic conditions. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  1. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Science.gov (United States)

    Castet, Jean-Francois; Saleh, Joseph H

    2013-01-01

    This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

  2. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Directory of Open Access Journals (Sweden)

    Jean-Francois Castet

    Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also

  3. Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments.

    Science.gov (United States)

    Yan, Ying; Yi, Grace Y

    2016-07-01

    Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.

  4. Lipid emulsion improves survival in animal models of local anesthetic toxicity: a meta-analysis.

    Science.gov (United States)

    Fettiplace, Michael R; McCabe, Daniel J

    2017-08-01

    The Lipid Emulsion Therapy workgroup, organized by the American Academy of Clinical Toxicology, recently conducted a systematic review, which subjectively evaluated lipid emulsion as a treatment for local anesthetic toxicity. We re-extracted data and conducted a meta-analysis of survival in animal models. We extracted survival data from 26 publications and conducted a random-effect meta-analysis based on odds ratio weighted by inverse variance. We assessed the benefit of lipid emulsion as an independent variable in resuscitative models (16 studies). We measured Cochran's Q for heterogeneity and I2 to determine variance contributed by heterogeneity. Finally, we conducted a funnel plot analysis and Egger's test to assess for publication bias in studies. Lipid emulsion reduced the odds of death in resuscitative models (OR =0.24; 95%CI: 0.1-0.56, p = .0012). Heterogeneity analysis indicated a homogenous distribution. Funnel plot analysis did not indicate publication bias in experimental models. Meta-analysis of animal data supports the use of lipid emulsion (in combination with other resuscitative measures) for the treatment of local anesthetic toxicity, specifically from bupivacaine. Our conclusion differed from the original review. Analysis of outliers reinforced the need for good life support measures (securement of airway and chest compressions) along with prompt treatment with lipid.

  5. Analysis of survival in breast cancer patients by using different parametric models

    Science.gov (United States)

    Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

    2017-09-01

    In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

  6. Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

    Science.gov (United States)

    Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

    2017-05-01

    Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

  7. A mixture model for the joint analysis of latent developmental trajectories and survival

    NARCIS (Netherlands)

    Klein Entink, R.H.; Fox, J.P.; Hout, A. van den

    2011-01-01

    A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in

  8. Attenuation caused by infrequently updated covariates in survival analysis

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Liestøl, Knut

    2003-01-01

    Attenuation; Cox regression model; Measurement errors; Survival analysis; Time-dependent covariates......Attenuation; Cox regression model; Measurement errors; Survival analysis; Time-dependent covariates...

  9. Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

    Directory of Open Access Journals (Sweden)

    Yu-sheng Cheng

    2014-01-01

    Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

  10. Individual patient data meta-analysis of survival data using Poisson regression models

    Directory of Open Access Journals (Sweden)

    Crowther Michael J

    2012-03-01

    Full Text Available Abstract Background An Individual Patient Data (IPD meta-analysis is often considered the gold-standard for synthesising survival data from clinical trials. An IPD meta-analysis can be achieved by either a two-stage or a one-stage approach, depending on whether the trials are analysed separately or simultaneously. A range of one-stage hierarchical Cox models have been previously proposed, but these are known to be computationally intensive and are not currently available in all standard statistical software. We describe an alternative approach using Poisson based Generalised Linear Models (GLMs. Methods We illustrate, through application and simulation, the Poisson approach both classically and in a Bayesian framework, in two-stage and one-stage approaches. We outline the benefits of our one-stage approach through extension to modelling treatment-covariate interactions and non-proportional hazards. Ten trials of hypertension treatment, with all-cause death the outcome of interest, are used to apply and assess the approach. Results We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard. Some downward bias is observed in classical estimates of the heterogeneity in the treatment effect, with improved performance from the Bayesian approach. Conclusion Our approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.

  11. Individual patient data meta-analysis of survival data using Poisson regression models.

    Science.gov (United States)

    Crowther, Michael J; Riley, Richard D; Staessen, Jan A; Wang, Jiguang; Gueyffier, Francois; Lambert, Paul C

    2012-03-23

    An Individual Patient Data (IPD) meta-analysis is often considered the gold-standard for synthesising survival data from clinical trials. An IPD meta-analysis can be achieved by either a two-stage or a one-stage approach, depending on whether the trials are analysed separately or simultaneously. A range of one-stage hierarchical Cox models have been previously proposed, but these are known to be computationally intensive and are not currently available in all standard statistical software. We describe an alternative approach using Poisson based Generalised Linear Models (GLMs). We illustrate, through application and simulation, the Poisson approach both classically and in a Bayesian framework, in two-stage and one-stage approaches. We outline the benefits of our one-stage approach through extension to modelling treatment-covariate interactions and non-proportional hazards. Ten trials of hypertension treatment, with all-cause death the outcome of interest, are used to apply and assess the approach. We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard. Some downward bias is observed in classical estimates of the heterogeneity in the treatment effect, with improved performance from the Bayesian approach. Our approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.

  12. Regression modeling strategies with applications to linear models, logistic and ordinal regression, and survival analysis

    CERN Document Server

    Harrell , Jr , Frank E

    2015-01-01

    This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap.  The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes.  This text realistically...

  13. Regression analysis of restricted mean survival time based on pseudo-observations

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Hansen, Mette Gerster; Klein, John P.

    censoring; hazard function; health economics; regression model; survival analysis; mean survival time; restricted mean survival time; pseudo-observations......censoring; hazard function; health economics; regression model; survival analysis; mean survival time; restricted mean survival time; pseudo-observations...

  14. Regression Analysis of Restricted Mean Survival Time Based on Pseudo-Observations

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Hansen, Mette Gerster; Klein, John P.

    2004-01-01

    censoring; hazard function; health economics; mean survival time; pseudo-observations; regression model; restricted mean survival time; survival analysis......censoring; hazard function; health economics; mean survival time; pseudo-observations; regression model; restricted mean survival time; survival analysis...

  15. Modeling survival data extending the cox model

    CERN Document Server

    Therneau, Terry M

    2000-01-01

    Extending the Cox Model is aimed at researchers, practitioners, and graduate students who have some exposure to traditional methods of survival analysis The emphasis is on semiparametric methods based on the proportional hazards model The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians

  16. Rethinking plant functional types in Earth System Models: pan-tropical analysis of tree survival across environmental gradients

    Science.gov (United States)

    Johnson, D. J.; Needham, J.; Xu, C.; Davies, S. J.; Bunyavejchewin, S.; Giardina, C. P.; Condit, R.; Cordell, S.; Litton, C. M.; Hubbell, S.; Kassim, A. R. B.; Shawn, L. K. Y.; Nasardin, M. B.; Ong, P.; Ostertag, R.; Sack, L.; Tan, S. K. S.; Yap, S.; McDowell, N. G.; McMahon, S.

    2016-12-01

    Terrestrial carbon cycling is a function of the growth and survival of trees. Current model representations of tree growth and survival at a global scale rely on coarse plant functional traits that are parameterized very generally. In view of the large biodiversity in the tropical forests, it is important that we account for the functional diversity in order to better predict tropical forest responses to future climate changes. Several next generation Earth System Models are moving towards a size-structured, trait-based approach to modelling vegetation globally, but the challenge of which and how many traits are necessary to capture forest complexity remains. Additionally, the challenge of collecting sufficient trait data to describe the vast species richness of tropical forests is enormous. We propose a more fundamental approach to these problems by characterizing forests by their patterns of survival. We expect our approach to distill real-world tree survival into a reasonable number of functional types. Using 10 large-area tropical forest plots that span geographic, edaphic and climatic gradients, we model tree survival as a function of tree size for hundreds of species. We found surprisingly few categories of size-survival functions emerge. This indicates some fundamental strategies at play across diverse forests to constrain the range of possible size-survival functions. Initial cluster analysis indicates that four to eight functional forms are necessary to describe variation in size-survival relations. Temporal variation in size-survival functions can be related to local environmental variation, allowing us to parameterize how demographically similar groups of species respond to perturbations in the ecosystem. We believe this methodology will yield a synthetic approach to classifying forest systems that will greatly reduce uncertainty and complexity in global vegetation models.

  17. Effects of temperature on development, survival and reproduction of insects: Experimental design, data analysis and modeling

    Science.gov (United States)

    Jacques Regniere; James Powell; Barbara Bentz; Vincent Nealis

    2012-01-01

    The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to...

  18. Modeling receptor kinetics in the analysis of survival data for organophosphorus pesticides.

    NARCIS (Netherlands)

    Jager, D.T.; Kooijman, S.A.L.M.

    2005-01-01

    Acute ecotoxicological tests usually focus on survival at a standardized exposure time. However, LC50's decrease in time in a manner that depends both on the chemical and on the organism. DEBtox is an existing approach to analyze toxicity data in time, based on hazard modeling (the internal

  19. Statistical analysis of survival data.

    Science.gov (United States)

    Crowley, J; Breslow, N

    1984-01-01

    A general review of the statistical techniques that the authors feel are most important in the analysis of survival data is presented. The emphasis is on the study of the duration of time between any two events as applied to people and on the nonparametric and semiparametric models most often used in these settings. The unifying concept is the hazard function, variously known as the risk, the force of mortality, or the force of transition.

  20. Mediation Analysis with Survival Outcomes: Accelerated Failure Time Versus Proportional Hazards Models

    Directory of Open Access Journals (Sweden)

    Lois A Gelfand

    2016-03-01

    Full Text Available Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH and fully parametric accelerated failure time (AFT approaches for illustration.Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively under varied data conditions, some including censoring. A simulated data set illustrates the findings.Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome – underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

  1. Survival analysis of stochastic competitive models in a polluted environment and stochastic competitive exclusion principle.

    Science.gov (United States)

    Liu, Meng; Wang, Ke; Wu, Qiong

    2011-09-01

    Stochastic competitive models with pollution and without pollution are proposed and studied. For the first system with pollution, sufficient criteria for extinction, nonpersistence in the mean, weak persistence in the mean, strong persistence in the mean, and stochastic permanence are established. The threshold between weak persistence in the mean and extinction for each population is obtained. It is found that stochastic disturbance is favorable for the survival of one species and is unfavorable for the survival of the other species. For the second system with pollution, sufficient conditions for extinction and weak persistence are obtained. For the model without pollution, a partial stochastic competitive exclusion principle is derived. © Society for Mathematical Biology 2010

  2. Gene-gene interaction analysis for the survival phenotype based on the Cox model.

    Science.gov (United States)

    Lee, Seungyeoun; Kwon, Min-Seok; Oh, Jung Mi; Park, Taesung

    2012-09-15

    For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP-SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene-gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR. Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene-gene interactions with the survival time. leesy@sejong.ac.kr; tspark@snu.ac.kr.

  3. Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data.

    Science.gov (United States)

    Tang, An-Min; Tang, Nian-Sheng; Zhu, Hongtu

    2017-04-30

    The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew-normal distribution. A Monte Carlo Expectation-Maximization (EM) algorithm together with the penalized-splines technique and the Metropolis-Hastings algorithm within the Gibbs sampler is developed to estimate parameters and nonparametric functions in the considered joint models. Case deletion diagnostic measures are proposed to identify the potential influential observations, and an extended local influence method is presented to assess local influence of minor perturbations. Simulation studies and a real example from a clinical trial are presented to illustrate the proposed methodologies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  4. Survival benefits of antiretroviral therapy in Brazil: a model-based analysis

    Science.gov (United States)

    Luz, Paula M; Girouard, Michael P; Grinsztejn, Beatriz; Freedberg, Kenneth A; Veloso, Valdilea G; Losina, Elena; Struchiner, Claudio J; MacLean, Rachel L; Parker, Robert A; Paltiel, A David; Walensky, Rochelle P

    2016-01-01

    Objective In Brazil, universal provision of antiretroviral therapy (ART) has been guaranteed free of charge to eligible HIV-positive patients since December 1996. We sought to quantify the survival benefits of ART attributable to this programme. Methods We used a previously published microsimulation model of HIV disease and treatment (CEPAC-International) and data from Brazil to estimate life expectancy increase for HIV-positive patients initiating ART in Brazil. We divided the period of 1997 to 2014 into six eras reflecting increased drug regimen efficacy, regimen availability and era-specific mean CD4 count at ART initiation. Patients were simulated first without ART and then with ART. The 2014-censored and lifetime survival benefits attributable to ART in each era were calculated as the product of the number of patients initiating ART in a given era and the increase in life expectancy attributable to ART in that era. Results In total, we estimated that 598,741 individuals initiated ART. Projected life expectancy increased from 2.7, 3.3, 4.1, 4.9, 5.5 and 7.1 years without ART to 11.0, 17.5, 20.7, 23.0, 25.3, and 27.0 years with ART in Eras 1 through 6, respectively. Of the total projected lifetime survival benefit of 9.3 million life-years, 16% (or 1.5 million life-years) has been realized as of December 2014. Conclusions Provision of ART through a national programme has led to dramatic survival benefits in Brazil, the majority of which are still to be realized. Improvements in initial and subsequent ART regimens and higher CD4 counts at ART initiation have contributed to these increasing benefits. PMID:27029828

  5. Survival analysis of gastric cancer patients using Cox model: a five year study

    Directory of Open Access Journals (Sweden)

    Biglarian A

    2009-08-01

    Full Text Available "n Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Background: Gastric cancer is the second most common cancer and known as the second cause of death due to cancers worldwide. Adenocarcinoma is the most fatal cancer in Iran and a patient with this kind of cancer, has a lower lifetime than others. In this research, the survival of patients with gastric carcinoma who were registered at Taleghani Hospital, were studied."n"nMethods: 291 patients with Gastric carcinoma who had received care, chemotherapy or chemoradiotherapy, at Taleghani Hospital in Tehran from 2002 to 2007 were studied as a historical cohort. Their survival rates and its relationship with 12 risk factors were assessed."n"nResults: Of the 291 patients with Gastric carcinoma, 70.1 percent were men and others (29.9% were women. The mean age of men was 62.26 years and of women was 59.32 years at the time of diagnosis. Most of patients (93.91% were advanced stage and metastasis. The Cox proportional hazards model showed that age at diagnosis, tumor stage and histology type with survival time had significant relationships (p=0.039, p=0.042 and p=0.032 respectively."n"n Conclusion: The five-year survival rate and median lifetime of gastric cancer patients who underwent chemotherapy or chemoradiotherapy are very

  6. Artillery Survivability Model

    Science.gov (United States)

    2016-06-01

    experiment mode also enables users to set their own design of experiment by manipulating an editable CSV file. The second one is a real-time mode that...renders a 3D virtual environment of a restricted battlefield where the survivability movements of an artillery company are visualized . This mode...provides detailed visualization of the simulation and enables future experimental uses of the simulation as a training tool. 14. SUBJECT TERMS

  7. Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

    Directory of Open Access Journals (Sweden)

    Raftery Adrian E

    2009-02-01

    Full Text Available Abstract Background Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test. Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p

  8. Empirical likelihood method in survival analysis

    CERN Document Server

    Zhou, Mai

    2015-01-01

    Add the Empirical Likelihood to Your Nonparametric ToolboxEmpirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN.The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empiric

  9. Modelling survival and connectivity of

    NARCIS (Netherlands)

    van der Molen, J.; van Beek, J.; Augustine, S.; Vansteenbrugge, L.; van Walraven, L.; van Langenberg, V.; van der Veer, H.W.; Hostens, K.; Pitois, S.; Robbens, J.

    2015-01-01

    Three different models were applied to study the reproduction, survival and dispersal of Mnemiopsis leidyi in the Scheldt estuaries and the southern North Sea: a high-resolution particle tracking model with passive particles, a low-resolution particle tracking model with a reproduction model

  10. A stochastic evolutionary model for survival dynamics

    Science.gov (United States)

    Fenner, Trevor; Levene, Mark; Loizou, George

    2014-09-01

    The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in different contexts. Here we propose a generative model to capture the essential dynamics of survival analysis, traditionally employed in clinical trials and reliability analysis in engineering. In our model, the only implicit assumption made is that the longer an actor has been in the system, the more likely it is to have failed. We derive a power-law distribution for the process and provide preliminary empirical evidence for the validity of the model from two well-known survival analysis data sets.

  11. Gene–gene interaction analysis for the survival phenotype based on the Cox model

    OpenAIRE

    Lee, Seungyeoun; Kwon, Min-Seok; Oh, Jung Mi; Park, Taesung

    2012-01-01

    Motivation: For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP–SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene–gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) metho...

  12. Survival and Stationary Distribution Analysis of a Stochastic Competitive Model of Three Species in a Polluted Environment.

    Science.gov (United States)

    Zhao, Yu; Yuan, Sanling; Ma, Junling

    2015-07-01

    In this paper, we develop and study a stochastic model for the competition of three species with a generalized dose-response function in a polluted environment. We first carry out the survival analysis and obtain sufficient conditions for the extinction, non-persistence, weak persistence in the mean, strong persistence in the mean and stochastic permanence. The threshold between weak persistence in the mean and extinction is established for each species. Then, using Hasminskii's methods and a Lyapunov function, we derive sufficient conditions for the existence of stationary distribution for each population. Numerical simulations are carried out to support our theoretical results, and some biological significance is presented.

  13. Relevance Vector Machine for Survival Analysis.

    Science.gov (United States)

    Kiaee, Farkhondeh; Sheikhzadeh, Hamid; Mahabadi, Samaneh Eftekhari

    2016-03-01

    An accelerated failure time (AFT) model has been widely used for the analysis of censored survival or failure time data. However, the AFT imposes the restrictive log-linear relation between the survival time and the explanatory variables. In this paper, we introduce a relevance vector machine survival (RVMS) model based on Weibull AFT model that enables the use of kernel framework to automatically learn the possible nonlinear effects of the input explanatory variables on target survival times. We take advantage of the Bayesian inference technique in order to estimate the model parameters. We also introduce two approaches to accelerate the RVMS training. In the first approach, an efficient smooth prior is employed that improves the degree of sparsity. In the second approach, a fast marginal likelihood maximization procedure is used for obtaining a sparse solution of survival analysis task by sequential addition and deletion of candidate basis functions. These two approaches, denoted by smooth RVMS and fast RVMS, typically use fewer basis functions than RVMS and improve the RVMS training time; however, they cause a slight degradation in the RVMS performance. We compare the RVMS and the two accelerated approaches with the previous sparse kernel survival analysis method on a synthetic data set as well as six real-world data sets. The proposed kernel survival analysis models have been discovered to be more accurate in prediction, although they benefit from extra sparsity. The main advantages of our proposed models are: 1) extra sparsity that leads to a better generalization and avoids overfitting; 2) automatic relevance sample determination based on data that provide more accuracy, in particular for highly censored survival data; and 3) flexibility to utilize arbitrary number and types of kernel functions (e.g., non-Mercer kernels and multikernel learning).

  14. Towards an Extended Evolutionary Game Theory with Survival Analysis and Agreement Algorithms for Modeling Uncertainty, Vulnerability, and Deception

    Science.gov (United States)

    Ma, Zhanshan (Sam)

    Competition, cooperation and communication are the three fundamental relationships upon which natural selection acts in the evolution of life. Evolutionary game theory (EGT) is a 'marriage' between game theory and Darwin's evolution theory; it gains additional modeling power and flexibility by adopting population dynamics theory. In EGT, natural selection acts as optimization agents and produces inherent strategies, which eliminates some essential assumptions in traditional game theory such as rationality and allows more realistic modeling of many problems. Prisoner's Dilemma (PD) and Sir Philip Sidney (SPS) games are two well-known examples of EGT, which are formulated to study cooperation and communication, respectively. Despite its huge success, EGT exposes a certain degree of weakness in dealing with time-, space- and covariate-dependent (i.e., dynamic) uncertainty, vulnerability and deception. In this paper, I propose to extend EGT in two ways to overcome the weakness. First, I introduce survival analysis modeling to describe the lifetime or fitness of game players. This extension allows more flexible and powerful modeling of the dynamic uncertainty and vulnerability (collectively equivalent to the dynamic frailty in survival analysis). Secondly, I introduce agreement algorithms, which can be the Agreement algorithms in distributed computing (e.g., Byzantine Generals Problem [6][8], Dynamic Hybrid Fault Models [12]) or any algorithms that set and enforce the rules for players to determine their consensus. The second extension is particularly useful for modeling dynamic deception (e.g., asymmetric faults in fault tolerance and deception in animal communication). From a computational perspective, the extended evolutionary game theory (EEGT) modeling, when implemented in simulation, is equivalent to an optimization methodology that is similar to evolutionary computing approaches such as Genetic algorithms with dynamic populations [15][17].

  15. A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.

    Science.gov (United States)

    Perperoglou, Aris

    2016-12-10

    Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

    Directory of Open Access Journals (Sweden)

    Brors Benedikt

    2010-01-01

    Full Text Available Abstract Background Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test. The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets. Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. Results To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. Conclusions Current methods of theme

  17. Model description and evaluation of the mark-recapture survival model used to parameterize the 2012 status and threats analysis for the Florida manatee (Trichechus manatus latirostris)

    Science.gov (United States)

    Langtimm, Catherine A.; Kendall, William L.; Beck, Cathy A.; Kochman, Howard I.; Teague, Amy L.; Meigs-Friend, Gaia; Peñaloza, Claudia L.

    2016-11-30

    This report provides supporting details and evidence for the rationale, validity and efficacy of a new mark-recapture model, the Barker Robust Design, to estimate regional manatee survival rates used to parameterize several components of the 2012 version of the Manatee Core Biological Model (CBM) and Threats Analysis (TA).  The CBM and TA provide scientific analyses on population viability of the Florida manatee subspecies (Trichechus manatus latirostris) for U.S. Fish and Wildlife Service’s 5-year reviews of the status of the species as listed under the Endangered Species Act.  The model evaluation is presented in a standardized reporting framework, modified from the TRACE (TRAnsparent and Comprehensive model Evaluation) protocol first introduced for environmental threat analyses.  We identify this new protocol as TRACE-MANATEE SURVIVAL and this model evaluation specifically as TRACE-MANATEE SURVIVAL, Barker RD version 1. The longer-term objectives of the manatee standard reporting format are to (1) communicate to resource managers consistent evaluation information over sequential modeling efforts; (2) build understanding and expertise on the structure and function of the models; (3) document changes in model structures and applications in response to evolving management objectives, new biological and ecological knowledge, and new statistical advances; and (4) provide greater transparency for management and research review.

  18. Neyman, Markov processes and survival analysis.

    Science.gov (United States)

    Yang, Grace

    2013-07-01

    J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

  19. Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

    Science.gov (United States)

    Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

    2016-10-01

    The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

  20. Survival analysis of orthodontic mini-implants.

    Science.gov (United States)

    Lee, Shin-Jae; Ahn, Sug-Joon; Lee, Jae Won; Kim, Seong-Hun; Kim, Tae-Woo

    2010-02-01

    Survival analysis is useful in clinical research because it focuses on comparing the survival distributions and the identification of risk factors. Our aim in this study was to investigate the survival characteristics and risk factors of orthodontic mini-implants with survival analyses. One hundred forty-one orthodontic patients (treated from October 1, 2000, to November 29, 2007) were included in this survival study. A total of 260 orthodontic mini-implants that had sandblasted (large grit) and acid-etched screw parts were placed between the maxillary second premolar and the first molar. Failures of the implants were recorded as event data, whereas implants that were removed because treatment ended and those that were not removed during the study period were recorded as censored data. A nonparametric life table method was used to visualize the hazard function, and Kaplan-Meier survival curves were generated to identify the variables associated with implant failure. Prognostic variables associated with implant failure were identified with the Cox proportional hazard model. Of the 260 implants, 22 failed. The hazard function for implant failure showed that the risk is highest immediately after placement. The survival function showed that the median survival time of orthodontic mini-implants is sufficient for relatively long orthodontic treatments. The Cox proportional hazard model identified that increasing age is a decisive factor for implant survival. The decreasing pattern of the hazard function suggested gradual osseointegration of orthodontic mini-implants. When implants are placed in a young patient, special caution is needed to lessen the increased probability of failure, especially immediately after placement.

  1. A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis

    Science.gov (United States)

    Malone, Patrick S.; Lamis, Dorian A.; Masyn, Katherine E.; Northrup, Thomas F.

    2010-01-01

    The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that "softer" drugs lead to "harder," illicit drugs. However, the gateway hypothesis has been notably difficult to…

  2. Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree.

    Science.gov (United States)

    Chao, Cheng-Min; Yu, Ya-Wen; Cheng, Bor-Wen; Kuo, Yao-Lung

    2014-10-01

    The aim of the paper is to use data mining technology to establish a classification of breast cancer survival patterns, and offers a treatment decision-making reference for the survival ability of women diagnosed with breast cancer in Taiwan. We studied patients with breast cancer in a specific hospital in Central Taiwan to obtain 1,340 data sets. We employed a support vector machine, logistic regression, and a C5.0 decision tree to construct a classification model of breast cancer patients' survival rates, and used a 10-fold cross-validation approach to identify the model. The results show that the establishment of classification tools for the classification of the models yielded an average accuracy rate of more than 90% for both; the SVM provided the best method for constructing the three categories of the classification system for the survival mode. The results of the experiment show that the three methods used to create the classification system, established a high accuracy rate, predicted a more accurate survival ability of women diagnosed with breast cancer, and could be used as a reference when creating a medical decision-making frame.

  3. Making relative survival analysis relatively easy.

    Science.gov (United States)

    Pohar, Maja; Stare, Janez

    2007-12-01

    In survival analysis we are interested in time from the beginning of an observation until certain event (death, relapse, etc.). We assume that the final event is well defined, so that we are never in doubt whether the final event has occurred or not. In practice this is not always true. If we are interested in cause-specific deaths, then it may sometimes be difficult or even impossible to establish the cause of death, or there may be different causes of death, making it impossible to assign death to just one cause. Suicides of terminal cancer patients are a typical example. In such cases, standard survival techniques cannot be used for estimation of mortality due to a certain cause. The cure to the problem are relative survival techniques which compare the survival experience in a study cohort to the one expected should they follow the background population mortality rates. This enables the estimation of the proportion of deaths due to a certain cause. In this paper, we briefly review some of the techniques to model relative survival, and outline a new fitting method for the additive model, which solves the problem of dependency of the parameter estimation on the assumption about the baseline excess hazard. We then direct the reader's attention to our R package relsurv that provides functions for easy and flexible fitting of all the commonly used relative survival regression models. The basic features of the package have been described in detail elsewhere, but here we additionally explain the usage of the new fitting method and the interface for using population mortality data freely available on the Internet. The combination of the package and the data sets provides a powerful informational tool in the hands of a skilled statistician/informatician.

  4. Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

    Science.gov (United States)

    Crowther, Michael J; Look, Maxime P; Riley, Richard D

    2014-09-28

    Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

  5. Shared Frailty Model for Left-Truncated Multivariate Survival Data

    DEFF Research Database (Denmark)

    Jensen, Henrik; Brookmeyer, Ron; Aaby, Peter

    multivariate survival data, left truncation, multiplicative hazard model, shared gamma frailty, conditional model, piecewise exponential model, childhood survival......multivariate survival data, left truncation, multiplicative hazard model, shared gamma frailty, conditional model, piecewise exponential model, childhood survival...

  6. The dChip survival analysis module for microarray data

    Directory of Open Access Journals (Sweden)

    Minvielle Stéphane

    2011-03-01

    Full Text Available Abstract Background Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. Results We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. Conclusions The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org.

  7. Survival analysis of piglet pre-weaning mortality

    OpenAIRE

    P. Carnier; E. Zanetti; F. Maretto; Cecchinato, A.

    2010-01-01

    Survival analysis methodology was applied in order to analyse sources of variation of preweaning survival time and to estimate variance components using data from a crossbred piglets population. A frailty sire model was used with the litter effect treated as an additional random source of variation. All the variables considered had a significant effect on survivability: sex, cross-fostering, parity of the nurse-sow and litter size. The variance estimates of sire and litter were closed to 0.08...

  8. Time series modeling of system self-assessment of survival

    Energy Technology Data Exchange (ETDEWEB)

    Lu, H.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    Self-assessment of survival for a system, subsystem or component is implemented by assessing conditional performance reliability in real-time, which includes modeling and analysis of physical performance data. This paper proposes a time series analysis approach to system self-assessment (prediction) of survival. In the approach, physical performance data are modeled in a time series. The performance forecast is based on the model developed and is converted to the reliability of system survival. In contrast to a standard regression model, a time series model, using on-line data, is suitable for the real-time performance prediction. This paper illustrates an example of time series modeling and survival assessment, regarding an excessive tool edge wear failure mode for a twist drill operation.

  9. Meta-analysis of survival prediction with Palliative Performance Scale.

    Science.gov (United States)

    Downing, Michael; Lau, Francis; Lesperance, Mary; Karlson, Nicholas; Shaw, Jack; Kuziemsky, Craig; Bernard, Steve; Hanson, Laura; Olajide, Lola; Head, Barbara; Ritchie, Christine; Harrold, Joan; Casarett, David

    2007-01-01

    This paper aims to reconcile the use of Palliative Performance Scale (PPSv2) for survival prediction in palliative care through an international collaborative study by five research groups. The study involves an individual patient data meta-analysis on 1,808 patients from four original datasets to reanalyze their survival patterns by age, gender, cancer status, and initial PPS score. Our findings reveal a strong association between PPS and survival across the four datasets. The Kaplan-Meier survival curves show each PPS level as distinct, with a strong ordering effect in which higher PPS levels are associated with increased length of survival. Using a stratified Cox proportional hazard model to adjust for study differences, we found females lived significantly longer than males, with a further decrease in hazard for females not diagnosed with cancer. Further work is needed to refine the reporting of survival times/probabilities and to improve prediction accuracy with the inclusion of other variables in the models.

  10. Survival analysis of piglet pre-weaning mortality

    Directory of Open Access Journals (Sweden)

    P. Carnier

    2010-04-01

    Full Text Available Survival analysis methodology was applied in order to analyse sources of variation of preweaning survival time and to estimate variance components using data from a crossbred piglets population. A frailty sire model was used with the litter effect treated as an additional random source of variation. All the variables considered had a significant effect on survivability: sex, cross-fostering, parity of the nurse-sow and litter size. The variance estimates of sire and litter were closed to 0.08 and 2 respectively and the heritability of pre-weaning survival was 0.03.

  11. Evaluating survival model performance: a graphical approach.

    Science.gov (United States)

    Mandel, M; Galai, N; Simchen, E

    2005-06-30

    In the last decade, many statistics have been suggested to evaluate the performance of survival models. These statistics evaluate the overall performance of a model ignoring possible variability in performance over time. Using an extension of measures used in binary regression, we propose a graphical method to depict the performance of a survival model over time. The method provides estimates of performance at specific time points and can be used as an informal test for detecting time varying effects of covariates in the Cox model framework. The method is illustrated on real and simulated data using Cox proportional hazard model and rank statistics. Copyright 2005 John Wiley & Sons, Ltd.

  12. Analysis of survival data from telemetry projects

    Science.gov (United States)

    Bunck, C.M.; Winterstein, S.R.; Pollock, K.H.

    1985-01-01

    Telemetry techniques can be used to study the survival rates of animal populations and are particularly suitable for species or settings for which band recovery models are not. Statistical methods for estimating survival rates and parameters of survival distributions from observations of radio-tagged animals will be described. These methods have been applied to medical and engineering studies and to the study of nest success. Estimates and tests based on discrete models, originally introduced by Mayfield, and on continuous models, both parametric and nonparametric, will be described. Generalizations, including staggered entry of subjects into the study and identification of mortality factors will be considered. Additional discussion topics will include sample size considerations, relocation frequency for subjects, and use of covariates.

  13. Modelling population-based cancer survival trends using join point models for grouped survival data.

    Science.gov (United States)

    Yu, Binbing; Huang, Lan; Tiwari, Ram C; Feuer, Eric J; Johnson, Karen A

    2009-04-01

    In the United States cancer as a whole is the second leading cause of death and a major burden to health care, thus the medical progress against cancer is a major public health goal. There are many individual studies to suggest that cancer treatment breakthroughs and early diagnosis have significantly improved the prognosis of cancer patients. To better understand the relationship between medical improvements and the survival experience for the patient population at large, it is useful to evaluate cancer survival trends on the population level, e.g., to find out when and how much the cancer survival rates changed. In this paper, we analyze the population-based grouped cancer survival data by incorporating joinpoints into the survival models. A joinpoint survival model facilitates the identification of trends with significant change points in cancer survival, when related to cancer treatments or interventions. The Bayesian Information Criterion is used to select the number of joinpoints. The performance of the joinpoint survival models is evaluated with respect to cancer prognosis, joinpoint locations, annual percent changes in death rates by year of diagnosis, and sample sizes through intensive simulation studies. The model is then applied to the grouped relative survival data for several major cancer sites from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute. The change points in the survival trends for several major cancer sites are identified and the potential driving forces behind such change points are discussed.

  14. SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

    Directory of Open Access Journals (Sweden)

    Masoud Asgharian

    2010-12-01

    Full Text Available When survival data are colleted as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. These prevalent cases are then followed for a fixed period of time at the end of which the subjects will either have failed or have been censored. When interests lies in estimating the survival distribution, from onset, of subjects with the disease, one must take into account that the survival times of the cases in a prevalent cohort study are left truncated. When it is possible to assume that there has not been any epidemic of the disease over the past period of time that covers the onset times of the subjects, one may assume that the underlying incidence process that generates the initiating event times is a stationary Poisson process. Under such assumption, the survival times of the recruited subjects are called “lengthbiased”. I discuss the challenges one is faced with in analyzing these type of data. To address the theoretical aspects of the work, I present asymptotic results for the NPMLE of the length-biased as well as the unbiased survival distribution. I also discuss estimating the unbiased survival function using only the follow-up time. This addresses the case that the onset times are either unknown or known with uncertainty. Some of our most recent work and open questions will be presented. These include some aspects of analysis of covariates, strong approximation, functional LIL and density estimation under length-biased sampling with right censoring. The results will be illustrated with survival data from patients with dementia, collected as part of the Canadian Study of Health and Aging (CSHA.

  15. Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

    Science.gov (United States)

    Wey, Andrew; Connett, John; Rudser, Kyle

    2015-07-01

    For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. Genetic analysis of the cumulative pseudo-survival rate during lactation of Holstein cattle in Japan by using random regression models.

    Science.gov (United States)

    Sasaki, O; Aihara, M; Nishiura, A; Takeda, H; Satoh, M

    2015-08-01

    Longevity is a crucial economic trait in the dairy farming industry. In this study, our objective was to develop a random regression model for genetic evaluation of survival. For the analysis, we used test-day records obtained for the first 5 lactations of 380,252 cows from 1,296 herds in Japan between 2001 and 2010; this data set was randomly divided into 7 subsets. The cumulative pseudo-survival rate (PSR) was determined according to whether a cow was alive (1) or absent (0) in her herd on the test day within each lactation group. Each lactation number was treated as an independent trait in a random regression multiple-trait model (MTM) or as a repeated measure in a random regression single-trait repeatability model (STRM). A proportional hazard model (PHM) was also developed as a piecewise-hazards model. The average (± standard deviation) heritability estimates of the PSR at 365 d in milk (DIM) among the 7 data sets in the first (LG1), second (LG2), and third to fifth lactations (LG3) of the MTM were 0.042±0.007, 0.070±0.012, and 0.084±0.007, respectively. The heritability estimate of the STRM was 0.038±0.004. The genetic correlations of PSR between distinct DIM within or between lactation groups were high when the interval between DIM was short. These results indicated that whereas the genetic factors contributing to the PSR between closely associated DIM would be similar even for different lactation numbers, the genetic factors contributing to PSR would differ between distinct lactation periods. The average (± standard deviation) effective heritability estimate based on the relative risk of the PHM among the 7 data sets was 0.068±0.009. The estimated breeding values (EBV) in LG1, LG2, LG3, the STRM, and the PHM were unbiased estimates of the genetic trend. The absolute values of the Spearman's rank correlation coefficients between the EBV of the relative risk of the PHM and the EBV of PSR at 365 DIM for LG1, LG2, LG3, and the STRM were 0.75, 0.87, 0

  17. Survival analysis for customer satisfaction: A case study

    Science.gov (United States)

    Hadiyat, M. A.; Wahyudi, R. D.; Sari, Y.

    2017-11-01

    Most customer satisfaction surveys are conducted periodically to track their dynamics. One of the goals of this survey was to evaluate the service design by recognizing the trend of satisfaction score. Many researchers recommended in redesigning the service when the satisfaction scores were decreasing, so that the service life cycle could be predicted qualitatively. However, these scores were usually set in Likert scale and had quantitative properties. Thus, they should also be analyzed in quantitative model so that the predicted service life cycle would be done by applying the survival analysis. This paper discussed a starting point for customer satisfaction survival analysis with a case study in healthcare service.

  18. Survival analysis of patients on maintenance hemodialysis

    Directory of Open Access Journals (Sweden)

    A Chandrashekar

    2014-01-01

    Full Text Available Despite the continuous improvement of dialysis technology and pharmacological treatment, mortality rates for dialysis patients are still high. A 2-year prospective study was conducted at a tertiary care hospital to determine the factors influencing survival among patients on maintenance hemodialysis. 96 patients with end-stage renal disease surviving more than 3 months on hemodialysis (8-12 h/week were studied. Follow-up was censored at the time of death or at the end of 2-year study period, whichever occurred first. Of the 96 patients studied (mean age 49.74 ± 14.55 years, 75% male and 44.7% diabetics, 19 died with an estimated mortality rate of 19.8%. On an age-adjusted multivariate analysis, female gender and hypokalemia independently predicted mortality. In Cox analyses, patient survival was associated with delivered dialysis dose (single pool Kt/V, hazard ratio [HR] =0.01, P = 0.016, frequency of hemodialysis (HR = 3.81, P = 0.05 and serum albumin (HR = 0.24, P = 0.005. There was no significant difference between diabetes and non-diabetes in relation to death (Relative Risk = 1.109; 95% CI = 0.49-2.48, P = 0.803. This study revealed that mortality among hemodialysis patients remained high, mostly due to sepsis and ischemic heart disease. Patient survival was better with higher dialysis dose, increased frequency of dialysis and adequate serum albumin level. Efforts at minimizing infectious complications, preventing cardiovascular events and improving nutrition should increase survival among hemodialysis patients.

  19. Direct Survival Analysis: a new stock assessment method

    Directory of Open Access Journals (Sweden)

    Eduardo Ferrandis

    2007-03-01

    Full Text Available In this work, a new stock assessment method, Direct Survival Analysis, is proposed and described. The parameter estimation of the Weibull survival model proposed by Ferrandis (2007 is obtained using trawl survey data. This estimation is used to establish a baseline survival function, which is in turn used to estimate the specific survival functions in the different cohorts considered through an adaptation of the separable model of the fishing mortality rates introduced by Pope and Shepherd (1982. It is thus possible to test hypotheses on the evolution of survival during the period studied and to identify trends in recruitment. A link is established between the preceding analysis of trawl survey data and the commercial catch-at-age data that are generally obtained to evaluate the population using analytical models. The estimated baseline survival, with the proposed versions of the stock and catch equations and the adaptation of the Separable Model, may be applied to commercial catch-at-age data. This makes it possible to estimate the survival corresponding to the landing data, the initial size of the cohort and finally, an effective age of first capture, in order to complete the parameter model estimation and consequently the estimation of the whole survival and mortality, along with the reference parameters that are useful for management purposes. Alternatively, this estimation of an effective age of first capture may be obtained by adapting the demographic structure of trawl survey data to that of the commercial fleet through suitable selectivity models of the commercial gears. The complete model provides the evaluation of the stock at any age. The coherence (and hence the mutual “calibration” between the two kinds of information may be analysed and compared with results obtained by other methods, such as virtual population analysis (VPA, in order to improve the diagnosis of the state of exploitation of the population. The model may be

  20. [Dealing with competing events in survival analysis].

    Science.gov (United States)

    Béchade, Clémence; Lobbedez, Thierry

    2015-04-01

    Survival analyses focus on the occurrences of an event of interest, in order to determine risk factors and estimate a risk. Competing events prevent from observing the event of interest. If there are competing events, it can lead to a bias in the risk's estimation. The aim of this article is to explain why Cox model is not appropriate when there are competing events, and to present Fine and Gray model, which can help when dealing with competing risks. Copyright © 2015 Association Société de néphrologie. Published by Elsevier SAS. All rights reserved.

  1. A generalized additive regression model for survival times

    DEFF Research Database (Denmark)

    Scheike, Thomas H.

    2001-01-01

    Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...

  2. FS5 sun exposure survivability analysis

    Directory of Open Access Journals (Sweden)

    Ming-Ying Hsu

    2017-01-01

    Full Text Available During the Acquisition and Safe Hold (ASH mode, FORMOAT-5 (FS5 satellite attitude is not fully controlled. Direct sun exposure on the Remote Sensing Instrument (RSI satellite telescope sensor may occur. The sun exposure effect on RSI sensor performance is investigated to evaluate the instrument’s survivability in orbit. Both satellite spin speed and sun exposure duration are considered as the key parameters in this study. A simple radiometry technique is used to calculate the total sun radiance exposure to examine the RSI sensor integrity. Total sun irradiance on the sensor is computed by considering the spectral variation effect through the RSI’s five-band filter. Experiments that directly expose the sensor to the sun on the ground were performed with no obvious performance degradation found. Based on both the analysis and experiment results, it is concluded that the FS5 RSI sensor can survive direct sun exposure during the ASH mode.

  3. CASE-CONTROL SURVIVAL ANALYSIS WITH A GENERAL SEMIPARAMETRIC SHARED FRAILTY MODEL - A PSEUDO FULL LIKELIHOOD APPROACH.

    Science.gov (United States)

    Gorfine, Malka; Zucker, David M; Hsu, Li

    2009-01-01

    In this work we deal with correlated failure time (age at onset) data arising from population-based case-control studies, where case and control probands are selected by population-based sampling and an array of risk factor measures is collected for both cases and controls and their relatives. Parameters of interest are effects of risk factors on the failure time hazard function and within-family dependencies among failure times after adjusting for the risk factors. Due to the retrospective sampling scheme, large sample theory for existing methods has not been established. We develop a novel technique for estimating the parameters of interest under a general semiparametric shared frailty model. We also present a simple, easily computed, and non-iterative nonparametric estimator for the cumulative baseline hazard function. We provide rigorous large sample theory for the proposed method. We also present simulation results and a real data example for illustrating the utility of the proposed method.

  4. Covariate analysis of bivariate survival data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, L.E.

    1992-01-01

    The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.

  5. Prognostic and survival analysis of presbyopia: The healthy twin study

    Science.gov (United States)

    Lira, Adiyani; Sung, Joohon

    2015-12-01

    Presbyopia, a vision condition in which the eye loses its flexibility to focus on near objects, is part of ageing process which mostly perceptible in the early or mid 40s. It is well known that age is its major risk factor, while sex, alcohol, poor nutrition, ocular and systemic diseases are known as common risk factors. However, many other variables might influence the prognosis. Therefore in this paper we developed a prognostic model to estimate survival from presbyopia. 1645 participants which part of the Healthy Twin Study, a prospective cohort study that has recruited Korean adult twins and their family members based on a nation-wide registry at public health agencies since 2005, were collected and analyzed by univariate analysis as well as Cox proportional hazard model to reveal the prognostic factors for presbyopia while survival curves were calculated by Kaplan-Meier method. Besides age, sex, diabetes, and myopia; the proposed model shows that education level (especially engineering program) also contribute to the occurrence of presbyopia as well. Generally, at 47 years old, the chance of getting presbyopia becomes higher with the survival probability is less than 50%. Furthermore, our study shows that by stratifying the survival curve, MZ has shorter survival with average onset time about 45.8 compare to DZ and siblings with 47.5 years old. By providing factors that have more effects and mainly associate with presbyopia, we expect that we could help to design an intervention to control or delay its onset time.

  6. The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin; Volume XII; A Multinomial Model for Estimating Ocean Survival from Salmonid Coded Wire-Tag Data.

    Energy Technology Data Exchange (ETDEWEB)

    Ryding, Kristen E.; Skalski, John R.

    1999-06-01

    The purpose of this report is to illustrate the development of a stochastic model using coded wire-tag (CWT) release and age-at-return data, in order to regress first year ocean survival probabilities against coastal ocean conditions and climate covariates.

  7. Vulnerability survival analysis: a novel approach to vulnerability management

    Science.gov (United States)

    Farris, Katheryn A.; Sullivan, John; Cybenko, George

    2017-05-01

    Computer security vulnerabilities span across large, enterprise networks and have to be mitigated by security engineers on a routine basis. Presently, security engineers will assess their "risk posture" through quantifying the number of vulnerabilities with a high Common Vulnerability Severity Score (CVSS). Yet, little to no attention is given to the length of time by which vulnerabilities persist and survive on the network. In this paper, we review a novel approach to quantifying the length of time a vulnerability persists on the network, its time-to-death, and predictors of lower vulnerability survival rates. Our contribution is unique in that we apply the cox proportional hazards regression model to real data from an operational IT environment. This paper provides a mathematical overview of the theory behind survival analysis methods, a description of our vulnerability data, and an interpretation of the results.

  8. Acute pancreatitis: analysis of factors influencing survival.

    Science.gov (United States)

    Jacobs, M L; Daggett, W M; Civette, J M; Vasu, M A; Lawson, D W; Warshaw, A L; Nardi, G L; Bartlett, M K

    1977-01-01

    Of patients with acute pancreatitis (AP), there remains a group who suffer life-threatening complications despite current modes of therapy. To identify factors which distinguish this group from the entire patient population, a retrospectiva analysis of 519 cases of AP occurring over a 5-year period was undertaken. Thirty-one per cent of these patients had a history of alcoholism and 47% had a history of biliary disease. The overall mortality was 12.9%. Of symptoms and signs recorded at the time of admission, hypotension, tachycardia, fever, abdominal mass, and abnormal examination of the lung fields correlated positively with increased mortality. Seven features of the initial laboratory examination correlated with increased mortality. Shock, massive colloid requirement, hypocalcemia, renal failure, and respiratory failure requiring endotracheal intubation were complications associated with the poorest prognosis. Among patients in this series with three or more of these clinical characteristics, maximal nonoperative treatment yielded a survival rate of 29%, compared to the 64% survival rate for a group of patients treated operatively with cholecystostomy, gastrostomy, feeding jejunostomy, and sump drainage of the lesser sac and retroperitoneum.

  9. Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes.

    Science.gov (United States)

    Kim, Jae-Hun; Ko, Eun Sook; Lim, Yaeji; Lee, Kyung Soo; Han, Boo-Kyung; Ko, Eun Young; Hahn, Soo Yeon; Nam, Seok Jin

    2017-03-01

    Purpose To determine the relationship between tumor heterogeneity assessed by means of magnetic resonance (MR) imaging texture analysis and survival outcomes in patients with primary breast cancer. Materials and Methods Between January and August 2010, texture analysis of the entire primary breast tumor in 203 patients was performed with T2-weighted and contrast material-enhanced T1-weighted subtraction MR imaging for preoperative staging. Histogram-based uniformity and entropy were calculated. To dichotomize texture parameters for survival analysis, the 10-fold cross-validation method was used to determine cutoff points in the receiver operating characteristic curve analysis. The Cox proportional hazards model and Kaplan-Meier analysis were used to determine the association of texture parameters and morphologic or volumetric information obtained at MR imaging or clinical-pathologic variables with recurrence-free survival (RFS). Results There were 26 events, including 22 recurrences (10 local-regional and 12 distant) and four deaths, with a mean follow-up time of 56.2 months. In multivariate analysis, a higher N stage (RFS hazard ratio, 11.15 [N3 stage]; P = .002, Bonferroni-adjusted α = .0167), triple-negative subtype (RFS hazard ratio, 16.91; P breast cancers that appeared more heterogeneous on T2-weighted images (higher entropy) and those that appeared less heterogeneous on contrast-enhanced T1-weighted subtraction images (lower entropy) exhibited poorer RFS. © RSNA, 2016 Online supplemental material is available for this article.

  10. Analysis of telomerase target gene expression effects from murine models in patient cohorts by homology translation and random survival forest modeling

    Directory of Open Access Journals (Sweden)

    Frederik Otzen Bagger

    2016-03-01

    Full Text Available Acute myeloid leukemia (AML is an aggressive and rapidly fatal blood cancer that affects patients of any age group. Despite an initial response to standard chemotherapy, most patients relapse and this relapse is mediated by leukemia stem cell (LSC populations. We identified a functional requirement for telomerase in sustaining LSC populations in murine models of AML and validated this requirement using an inhibitor of telomerase in human AML. Here, we describe in detail the contents, quality control and methods of the gene expression analysis used in the published study (Gene Expression Omnibus GSE63242. Additionally, we provide annotated gene lists of telomerase regulated genes in AML and R code snippets to access and analyze the data used in the original manuscript.

  11. Evaluating disease management program effectiveness: an introduction to survival analysis.

    Science.gov (United States)

    Linden, Ariel; Adams, John L; Roberts, Nancy

    2004-01-01

    Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

  12. Comparison of Cox and Gray's survival models in severe sepsis

    DEFF Research Database (Denmark)

    Kasal, Jan; Andersen, Zorana Jovanovic; Clermont, Gilles

    2004-01-01

    Although survival is traditionally modeled using Cox proportional hazards modeling, this approach may be inappropriate in sepsis, in which the proportional hazards assumption does not hold. Newer, more flexible models, such as Gray's model, may be more appropriate.......Although survival is traditionally modeled using Cox proportional hazards modeling, this approach may be inappropriate in sepsis, in which the proportional hazards assumption does not hold. Newer, more flexible models, such as Gray's model, may be more appropriate....

  13. Mathematical Methods in Survival Analysis, Reliability and Quality of Life

    CERN Document Server

    Huber, Catherine; Mesbah, Mounir

    2008-01-01

    Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

  14. Extensions and applications of the Cox-Aalen survival model.

    Science.gov (United States)

    Scheike, Thomas H; Zhang, Mei-Jie

    2003-12-01

    Cox's regression model is the standard regression tool for survival analysis in most applications. Often, however, the model only provides a rough summary of the effect of some covariates. Therefore, if the aim is to give a detailed description of covariate effects and to consequently calculate predicted probabilities, more flexible models are needed. In another article, Scheike and Zhang (2002, Scandinavian Journal of Statistics 29, 75-88), we suggested a flexible extension of Cox's regression model, which aimed at extending the Cox model only for those covariates where additional flexibility are needed. One important advantage of the suggested approach is that even though covariates are allowed a nonparametric effect, the hassle and difficulty of finding smoothing parameters are not needed. We show how the extended model also leads to simple formulae for predicted probabilities and their standard errors, for example, in the competing risk framework.

  15. [Clinical research XXI. From the clinical judgment to survival analysis].

    Science.gov (United States)

    Rivas-Ruiz, Rodolfo; Pérez-Rodríguez, Marcela; Palacios, Lino; Talavera, Juan O

    2014-01-01

    Decision making in health care implies knowledge of the clinical course of the disease. Knowing the course allows us to estimate the likelihood of occurrence of a phenomenon at a given time or its duration. Within the statistical models that allow us to have a summary measure to estimate the time of occurrence of a phenomenon in a given population are the linear regression (the outcome variable is continuous and normally distributed -time to the occurrence of the event-), logistic regression (outcome variable is dichotomous, and it is evaluated at one single interval), and survival curves (outcome event is dichotomous, and it can be evaluated at multiple intervals). The first reference we have of this type of analysis is the work of the astronomer Edmond Halley, an English physicist and mathematician, famous for the calculation of the appearance of the comet orbit, recognized as the first periodic comet (1P/Halley's Comet). Halley also contributed in the area of health to estimate the mortality rate for a Polish population. The survival curve allows us to estimate the probability of an event occurring at different intervals. Also, it leds us to estimate the median survival time of any phenomenon of interest (although the used term is survival, the outcome does not need to be death, it may be the occurrence of any other event).

  16. FCS Vehicle Transportability, Survivability, and Reliability Analysis

    National Research Council Canada - National Science Library

    Dion-Schwarz, Cynthia; Hirsch, Leon; Koehn, Phillip; Macheret, Jenya; Sparrow, Dave

    2005-01-01

    .... The investigation into metrics for transportability revealed that the C130 Transportability requirement for FCS vehicles is a constraint that leads to a less survivable platform but without improving Unit of Action (UA) transportability...

  17. Graphics and statistics for cardiology: survival analysis.

    Science.gov (United States)

    May, Susanne; McKnight, Barbara

    2017-03-01

    Reports of data in the medical literature frequently lack information needed to assess the validity and generalisability of study results. Some recommendations and standards for reporting have been developed over the last two decades, but few are available specifically for survival data. We provide recommendations for tabular and graphical representations of survival data. We argue that data and analytic software should be made available to promote reproducible research. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  18. Analyzing sickness absence with statistical models for survival data

    DEFF Research Database (Denmark)

    Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars

    2007-01-01

    OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS...

  19. Survival analysis of patients under chronic HIV-care and ...

    African Journals Online (AJOL)

    Background: Health care planning depends upon good knowledge of prevalence that requires a clear understanding of survival patterns of patients who receive medication, treatment and care. Survival analysis can bring to light the effect that some demographic, social, medical and clinical characteristics have on the ...

  20. Potential density and tree survival: an analysis based on South ...

    African Journals Online (AJOL)

    Finally, we present a tree survival analysis, based on the Weibull distribution function, for the Nelshoogte replicated CCT study, which has been observed for almost 40 years after planting and provides information about tree survival in response to planting espacements ranging from 494 to 2 965 trees per hectare.

  1. Multiple imputation of missing blood pressure covariates in survival analysis

    NARCIS (Netherlands)

    Buuren, S. van; Boshuizen, H.C.; Knook, D.L.

    1999-01-01

    This paper studies a non-response problem in survival analysis where the occurrence of missing data in the risk factor is related to mortality. In a study to determine the influence of blood pressure on survival in the very old (85+ years), blood pressure measurements are missing in about 12.5 per

  2. Survival analysis of mortality data among elderly patients in ...

    African Journals Online (AJOL)

    A study on the mortality among old patients 60 years or more, admitted at University of Ilorin Teaching Hospital (UITH), Ilorin was carried out using survival analysis approach. Results revealed that the median survival time, which is the time beyond which half of the patients are expected to stay in hospital before death was ...

  3. Efficient estimation of semiparametric copula models for bivariate survival data

    KAUST Repository

    Cheng, Guang

    2014-01-01

    A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.

  4. SEMI-COMPETING RISKS ON A TRIVARIATE WEIBULL SURVIVAL MODEL

    Directory of Open Access Journals (Sweden)

    Jenq-Daw Lee

    2008-07-01

    Full Text Available A setting of a trivairate survival function using semi-competing risks concept is proposed, in which a terminal event can only occur after other events. The Stanford Heart Transplant data is reanalyzed using a trivariate Weibull distribution model with the proposed survival function.

  5. A life-cycle model with ambiguous survival beliefs

    NARCIS (Netherlands)

    Groneck, Max; Ludwig, Alexander; Zimper, Alexander

    Based on a cognitive notion of neo-additive capacities reflecting likelihood insensitivity with respect to survival chances, we construct a Choquet Bayesian learning model over the life-cycle that generates a motivational notion of neo-additive survival beliefs expressing ambiguity attitudes. We

  6. Prediction of survival with alternative modeling techniques using pseudo values

    NARCIS (Netherlands)

    T. van der Ploeg (Tjeerd); F.R. Datema (Frank); R.J. Baatenburg de Jong (Robert Jan); E.W. Steyerberg (Ewout)

    2014-01-01

    textabstractBackground: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo

  7. Pseudo-observations in survival analysis

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Perme, Maja Pohar

    2010-01-01

    -state models, e.g. the competing risks cumulative incidence function. Graphical and numerical methods for assessing goodness-of-fit for hazard regression models and for the Fine-Gray model in competing risks studies based on pseudo-observations are also reviewed. Sensitivity to covariate-dependent censoring...... is studied. The methods are illustrated using a data set from bone marrow transplantation....

  8. A hierarchical nest survival model integrating incomplete temporally varying covariates

    Science.gov (United States)

    Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.

    2013-01-01

    Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the

  9. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis.

    Science.gov (United States)

    Mittal, Sushil; Madigan, David; Burd, Randall S; Suchard, Marc A

    2014-04-01

    Survival analysis endures as an old, yet active research field with applications that spread across many domains. Continuing improvements in data acquisition techniques pose constant challenges in applying existing survival analysis methods to these emerging data sets. In this paper, we present tools for fitting regularized Cox survival analysis models on high-dimensional, massive sample-size (HDMSS) data using a variant of the cyclic coordinate descent optimization technique tailored for the sparsity that HDMSS data often present. Experiments on two real data examples demonstrate that efficient analyses of HDMSS data using these tools result in improved predictive performance and calibration.

  10. Survival

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — These data provide information on the survival of California red-legged frogs in a unique ecosystem to better conserve this threatened species while restoring...

  11. A gradient boosting algorithm for survival analysis via direct optimization of concordance index.

    Science.gov (United States)

    Chen, Yifei; Jia, Zhenyu; Mercola, Dan; Xie, Xiaohui

    2013-01-01

    Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online.

  12. Survival analysis and classification methods for forest fire size.

    Directory of Open Access Journals (Sweden)

    Pier-Olivier Tremblay

    Full Text Available Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene and the size at "being held" (a state when no further increase in size is expected. We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment. Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

  13. Survival analysis and classification methods for forest fire size.

    Science.gov (United States)

    Tremblay, Pier-Olivier; Duchesne, Thierry; Cumming, Steven G

    2018-01-01

    Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at "being held" (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

  14. [Analysis of survival and mortality curves with the model of vital receptors. The maximal life span. Effect of temperature on the life span. The mortality probability density function (mortality curve) and its parameters].

    Science.gov (United States)

    Poltorakov, A P

    2001-01-01

    We have continued an analysis of survival curves by the model of the vital receptors (MVR). The main types survival function (E-, TW- and GM-distributions) have been considered. It was found that the maximal life span depends on the threshold concentration of vital receptors. Equations are obtained for the dependence of the maximal life span on the kinetic parameters in the reactions of inactivation, destruction and inactivation. Dependence of maximal time life on initial size of the population have been considered. The influence of temperature on the survival curves is analysed by E-distribution. Equations are founded for the description of thermosurvival and thermoinactivation curves. Equation are obtained for the dependence of density function and it characteristics (modal and antimodal age, coefficient of asymmetry) on the MVR parameters. It was shown that E-, TW- and GM-distribution has different types of asymmetry. The coefficient of asymmetry of GM-distribution is associated on the MVR parameters. It is assumed that symmetry of the curves of mortality and birth-rate is coordinated by the mechanisms of MVR.

  15. Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals.

    Science.gov (United States)

    Zhu, Xiaoyan; Zhou, Xiaobin; Zhang, Yuan; Sun, Xiao; Liu, Haihua; Zhang, Yingying

    2017-12-01

    Survival analysis methods have gained widespread use in the filed of oncology. For achievement of reliable results, the methodological process and report quality is crucial. This review provides the first examination of methodological characteristics and reporting quality of survival analysis in articles published in leading Chinese oncology journals.To examine methodological and reporting quality of survival analysis, to identify some common deficiencies, to desirable precautions in the analysis, and relate advice for authors, readers, and editors.A total of 242 survival analysis articles were included to be evaluated from 1492 articles published in 4 leading Chinese oncology journals in 2013. Articles were evaluated according to 16 established items for proper use and reporting of survival analysis.The application rates of Kaplan-Meier, life table, log-rank test, Breslow test, and Cox proportional hazards model (Cox model) were 91.74%, 3.72%, 78.51%, 0.41%, and 46.28%, respectively, no article used the parametric method for survival analysis. Multivariate Cox model was conducted in 112 articles (46.28%). Follow-up rates were mentioned in 155 articles (64.05%), of which 4 articles were under 80% and the lowest was 75.25%, 55 articles were100%. The report rates of all types of survival endpoint were lower than 10%. Eleven of 100 articles which reported a loss to follow-up had stated how to treat it in the analysis. One hundred thirty articles (53.72%) did not perform multivariate analysis. One hundred thirty-nine articles (57.44%) did not define the survival time. Violations and omissions of methodological guidelines included no mention of pertinent checks for proportional hazard assumption; no report of testing for interactions and collinearity between independent variables; no report of calculation method of sample size. Thirty-six articles (32.74%) reported the methods of independent variable selection. The above defects could make potentially inaccurate

  16. In-season retail sales forecasting using survival models | Hattingh ...

    African Journals Online (AJOL)

    In order to identify products that should be marked down, the Retailer forecasts future sales of new products. With the aim of improving on the Retailer's current sales forecasting method, this study investigates statistical techniques, viz. classical time series analysis (Holt's smoothing method) and survival analysis. Forecasts ...

  17. Survival Analysis of Patients with End Stage Renal Disease

    Science.gov (United States)

    Urrutia, J. D.; Gayo, W. S.; Bautista, L. A.; Baccay, E. B.

    2015-06-01

    This paper provides a survival analysis of End Stage Renal Disease (ESRD) under Kaplan-Meier Estimates and Weibull Distribution. The data were obtained from the records of V. L. MakabaliMemorial Hospital with respect to time t (patient's age), covariates such as developed secondary disease (Pulmonary Congestion and Cardiovascular Disease), gender, and the event of interest: the death of ESRD patients. Survival and hazard rates were estimated using NCSS for Weibull Distribution and SPSS for Kaplan-Meier Estimates. These lead to the same conclusion that hazard rate increases and survival rate decreases of ESRD patient diagnosed with Pulmonary Congestion, Cardiovascular Disease and both diseases with respect to time. It also shows that female patients have a greater risk of death compared to males. The probability risk was given the equation R = 1 — e-H(t) where e-H(t) is the survival function, H(t) the cumulative hazard function which was created using Cox-Regression.

  18. Novel head and neck cancer survival analysis approach: random survival forests versus Cox proportional hazards regression.

    Science.gov (United States)

    Datema, Frank R; Moya, Ana; Krause, Peter; Bäck, Thomas; Willmes, Lars; Langeveld, Ton; Baatenburg de Jong, Robert J; Blom, Henk M

    2012-01-01

    Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modeling. Automatization of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive capability more reliable. Cox proportional hazard regression is most frequently used in prognostication. Automatization of a Cox model is possible, but we expect the updating process to be time-consuming. A possible solution lies in an alternative modeling technique called random survival forests (RSFs). RSF is easily automated and is known to handle the proportionality assumption coherently and automatically. Performance of RSF has not yet been tested on a large head and neck oncological dataset. This study investigates performance of head and neck overall survival of RSF models. Performances are compared to a Cox model as the "gold standard." RSF might be an interesting alternative modeling approach for automatization when performances are similar. RSF models were created in R (Cox also in SPSS). Four RSF splitting rules were used: log-rank, conservation of events, log-rank score, and log-rank approximation. Models were based on historical data of 1371 patients with primary head-and-neck cancer, diagnosed between 1981 and 1998. Models contain 8 covariates: tumor site, T classification, N classification, M classification, age, sex, prior malignancies, and comorbidity. Model performances were determined by Harrell's concordance error rate, in which 33% of the original data served as a validation sample. RSF and Cox models delivered similar error rates. The Cox model performed slightly better (error rate, 0.2826). The log-rank splitting approach gave the best RSF performance (error rate, 0.2873). In accord with Cox and RSF models, high T classification, high N classification, and severe comorbidity are very important covariates in the

  19. Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

    Directory of Open Access Journals (Sweden)

    Dahl Edgar

    2010-10-01

    Full Text Available Abstract Background We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. Results Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk along with the respective sets of compound markers. Conclusions The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.

  20. Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

    Science.gov (United States)

    2010-01-01

    Background We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. Results Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. Conclusions The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers. PMID:21034433

  1. Nonparametric survival analysis of infectious disease data.

    Science.gov (United States)

    Kenah, Eben

    2013-03-01

    This paper develops nonparametric methods based on contact intervals for the analysis of infectious disease data. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. The hazard function of the contact interval distribution equals the hazard of infectious contact from i to j, so it provides a summary of the evolution of infectiousness over time. When who-infects-whom is observed, the Nelson-Aalen estimator produces an unbiased estimate of the cumulative hazard function of the contact interval distribution. When who-infects-whom is not observed, we use an EM algorithm to average the Nelson-Aalen estimates from all possible combinations of who-infected-whom consistent with the observed data. This converges to a nonparametric maximum likelihood estimate of the cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic.

  2. Nonparametric survival analysis of infectious disease data

    Science.gov (United States)

    Kenah, Eben

    2012-01-01

    Summary This paper develops nonparametric methods based on contact intervals for the analysis of infectious disease data. The contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j, where we define infectious contact as a contact sufficient to infect a susceptible individual. The hazard function of the contact interval distribution equals the hazard of infectious contact from i to j, so it provides a summary of the evolution of infectiousness over time. When who-infects-whom is observed, the Nelson-Aalen estimator produces an unbiased estimate of the cumulative hazard function of the contact interval distribution. When who-infects-whom is not observed, we use an EM algorithm to average the Nelson-Aalen estimates from all possible combinations of who-infected-whom consistent with the observed data. This converges to a nonparametric maximum likelihood estimate of the cumulative hazard function that we call the marginal Nelson-Aalen estimate. We study the behavior of these methods in simulations and use them to analyze household surveillance data from the 2009 influenza A(H1N1) pandemic. PMID:23772180

  3. A Log Logistic Survival Model Applied to Hypobaric Decompression Sickness

    Science.gov (United States)

    Conkin, Johnny

    2001-01-01

    Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.

  4. Prediction of survival for older hospitalized patients: the HELP survival model. Hospitalized Elderly Longitudinal Project.

    Science.gov (United States)

    Teno, J M; Harrell, F E; Knaus, W; Phillips, R S; Wu, A W; Connors, A; Wenger, N S; Wagner, D; Galanos, A; Desbiens, N A; Lynn, J

    2000-05-01

    To develop and validate a model estimating the survival time of hospitalized persons aged 80 years and older. A prospective cohort study with mortality follow-up using the National Death Index. Four teaching hospitals in the US. Hospitalized patients enrolled between January 1993 and November 1994 in the Hospitalized Elderly Longitudinal Project (HELP). Patients were excluded if their length of hospital stay was 48 hours or less or if admitted electively for planned surgery. A log-normal model of survival time up to 711 days was developed with the following variables: patient demographics, disease category, nursing home residence, severity of physiologic imbalance, chart documentation of weight loss, current quality of life, exercise capacity, and functional status. We assessed whether model accuracy could be improved by including symptoms of depression or history of recent fall, serum albumin, physician's subjective estimate of prognosis, and physician and patient preferences for general approach to care. A total of 1266 patients were enrolled over a 10-month period, (median age 84.9, 61% female, 68% with one or more dependency), and 505 (40%) died during an average follow-up of more than 2 years. Important prognostic factors included the Acute Physiology Score of APACHE III collected on the third hospital day, modified Glasgow coma score, major diagnosis (ICU categories together, congestive heart failure, cancer, orthopedic, and all other), age, activities of daily living, exercise capacity, chart documentation of weight loss, and global quality of life. The Somers' Dxy for a model including these factors was 0.48 (equivalent to a receiver-operator curve (ROC) area of 0.74, suggesting good discrimination). Bootstrap estimation indicated good model validation (corrected Dxy of 0.46, ROC of 0.73). A nomogram based on this log-normal model is presented to facilitate calculation of median survival time and 10th and 90th percentile of survival time. A count of

  5. Survival analysis using S analysis of time-to-event data

    CERN Document Server

    Tableman, Mara

    2003-01-01

    Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter ...

  6. LONG TERM SURVIVAL FOLLOWING TRAUMATIC BRAIN INJURY: A POPULATION BASED PARAMETRIC SURVIVAL ANALYSIS

    Science.gov (United States)

    Fuller, Gordon Ward; Ransom, Jeanine; Mandrekar, Jay; Brown, Allen W

    2017-01-01

    Background Long term mortality may be increased following traumatic brain injury (TBI); however the degree to which survival could be reduced is unknown. We aimed to model life expectancy following post-acute TBI to provide predictions of longevity and quantify differences in survivorship with the general population. Methods A population based retrospective cohort study using data from the Rochester Epidemiology Project (REP) was performed. A random sample of patients from Olmsted County, Minnesota with a confirmed TBI between 1987 and 2000 was identified and vital status determined in 2013. Parametric survival modelling was then used to develop a model to predict life expectancy following TBI conditional on age at injury. Survivorship following TBI was also compared with the general population and age and gender matched non-head injured REP controls. Results 769 patients were included in complete case analyses. Median follow up time was 16.1 years (IQR 9.0–20.4) with 120 deaths occurring in the cohort during the study period. Survival after acute TBI was well represented by a Gompertz distribution. Victims of TBI surviving for at least 6 months post-injury demonstrated a much higher ongoing mortality rate compared to the US general population and non-TBI controls (hazard ratio 1·47, 95% CI 1·15–1·87). US general population cohort life table data was used to update the Gompertz model’s shape and scale parameters to account for cohort effects and allow prediction of life expectancy in contemporary TBI. Conclusions Survivors of TBI have decreased life expectancy compared to the general population. This may be secondary to the head injury itself or result from patient characteristics associated with both the propensity for TBI and increased early mortality. Post-TBI life expectancy estimates may be useful to guide prognosis, in public health planning, for actuarial applications and in the extrapolation of outcomes for TBI economic models. PMID:27165161

  7. A simple prognostic model for overall survival in metastatic renal cell carcinoma

    Science.gov (United States)

    Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

    2016-01-01

    Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

  8. Survival analysis of preweaning piglet survival in a dry-cured ham-producing crossbred line.

    Science.gov (United States)

    Cecchinato, A; Bonfatti, V; Gallo, L; Carnier, P

    2008-10-01

    The aim of this study was to investigate piglet preweaning survival and its relationship with a total merit index (TMI) used for selection of Large White terminal boars for dry-cured ham production. Data on 13,924 crossbred piglets (1,347 litters), originated by 189 Large White boars and 328 Large White-derived crossbred sows, were analyzed under a frailty proportional hazards model, assuming different baseline hazard functions and including sire and nursed litter as random effects. Estimated hazard ratios (HR) indicated that sex, cross-fostering, year-month of birth, parity of the nurse sow, size of the nursed litter, and class of TMI were significant effects for piglet preweaning survival. Female piglets had less risk of dying than males (HR = 0.81), as well as cross-fostered piglets (HR = 0.60). Survival increased when piglets were nursed by sows of third (HR = 0.85), fourth (HR = 0.76), and fifth (HR = 0.79) parity in comparison with first and second parity sows. Piglets of small (HR = 3.90) or very large litters (HR >1.60) had less chance of surviving in comparison with litters of intermediate size. Class of TMI exhibited an unfavorable relationship with survival (HR = 1.20 for the TMI top class). The modal estimates of sire variance under different baseline hazard functions were 0.06, whereas the variance for the nursed litter was close to 0.7. The estimate of the nursed litter effect variance was greater than that of the sire, which shows the importance of the common environment generated by the nurse sow. Relationships between sire rankings obtained from different survival models were high. The heritability estimate in equivalent scale was low and reached a value of 0.03. Nevertheless, the exploitable genetic variation for this trait justifies the inclusion of piglet preweaning survival in the current breeding program for selection of Large White terminal boars for dry-cured ham production.

  9. Regression models for interval censored survival data: Application to HIV infection in Danish homosexual men

    DEFF Research Database (Denmark)

    Carstensen, Bendix

    1996-01-01

    This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men....

  10. Modeling the airborne survival of influenza virus in a residential setting: the impacts of home humidification

    Directory of Open Access Journals (Sweden)

    Myatt Theodore A

    2010-09-01

    Full Text Available Abstract Background Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH conditions. Further analysis has shown that absolute humidity (AH may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza. Methods A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival. Results The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival. Conclusion This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity

  11. Survival analysis of cervical cancer using stratified Cox regression

    Science.gov (United States)

    Purnami, S. W.; Inayati, K. D.; Sari, N. W. Wulan; Chosuvivatwong, V.; Sriplung, H.

    2016-04-01

    Cervical cancer is one of the mostly widely cancer cause of the women death in the world including Indonesia. Most cervical cancer patients come to the hospital already in an advanced stadium. As a result, the treatment of cervical cancer becomes more difficult and even can increase the death's risk. One of parameter that can be used to assess successfully of treatment is the probability of survival. This study raises the issue of cervical cancer survival patients at Dr. Soetomo Hospital using stratified Cox regression based on six factors such as age, stadium, treatment initiation, companion disease, complication, and anemia. Stratified Cox model is used because there is one independent variable that does not satisfy the proportional hazards assumption that is stadium. The results of the stratified Cox model show that the complication variable is significant factor which influent survival probability of cervical cancer patient. The obtained hazard ratio is 7.35. It means that cervical cancer patient who has complication is at risk of dying 7.35 times greater than patient who did not has complication. While the adjusted survival curves showed that stadium IV had the lowest probability of survival.

  12. Breastfeeding, birth intervals and child survival: analysis of the 1997 ...

    African Journals Online (AJOL)

    Original article. Breastfeeding, birth intervals and child survival: analysis of the 1997 community and family survey data in southern Ethiopia. Markos Ezra, Eshetu Gurmu. Abstract. Background: This paper uses the 1997 community and family survey data to primarily address the question of whether or not short birth intervals ...

  13. Use of parametric and non-parametric survival analysis techniques ...

    African Journals Online (AJOL)

    This paper presents parametric and non-parametric survival analysis procedures that can be used to compare acaricides. The effectiveness of Delta Tick Pour On and Delta Tick Spray in knocking down tsetse flies were determined. The two formulations were supplied by Chemplex. The comparison was based on data ...

  14. Using Survival Analysis to Understand Graduation of Students with Disabilities

    Science.gov (United States)

    Schifter, Laura A.

    2016-01-01

    This study examined when students with disabilities graduated high school and how graduation patterns differed for students based on selected demographic and educational factors. Utilizing statewide data on students with disabilities from Massachusetts from 2005 through 2012, the author conducted discrete-time survival analysis to estimate the…

  15. Integrative Genomics with Mediation Analysis in a Survival Context

    Directory of Open Access Journals (Sweden)

    Szilárd Nemes

    2013-01-01

    Full Text Available DNA copy number aberrations (DCNA and subsequent altered gene expression profiles may have a major impact on tumor initiation, on development, and eventually on recurrence and cancer-specific mortality. However, most methods employed in integrative genomic analysis of the two biological levels, DNA and RNA, do not consider survival time. In the present note, we propose the adoption of a survival analysis-based framework for the integrative analysis of DCNA and mRNA levels to reveal their implication on patient clinical outcome with the prerequisite that the effect of DCNA on survival is mediated by mRNA levels. The specific aim of the paper is to offer a feasible framework to test the DCNA-mRNA-survival pathway. We provide statistical inference algorithms for mediation based on asymptotic results. Furthermore, we illustrate the applicability of the method in an integrative genomic analysis setting by using a breast cancer data set consisting of 141 invasive breast tumors. In addition, we provide implementation in R.

  16. Tutorial: survival analysis--a statistic for clinical, efficacy, and theoretical applications.

    Science.gov (United States)

    Gruber, F A

    1999-04-01

    Current demands for increased research attention to therapeutic efficacy, efficiency, and also for improved developmental models call for analysis of longitudinal outcome data. Statistical treatment of longitudinal speech and language data is difficult, but there is a family of statistical techniques in common use in medicine, actuarial science, manufacturing, and sociology that has not been used in speech or language research. Survival analysis is introduced as a method that avoids many of the statistical problems of other techniques because it treats time as the outcome. In survival analysis, probabilities are calculated not just for groups but also for individuals in a group. This is a major advantage for clinical work. This paper provides a basic introduction to nonparametric and semiparametric survival analysis using speech outcomes as examples. A brief discussion of potential conflicts between actuarial analysis and clinical intuition is also provided.

  17. ESTIMATION OF SURVIVAL FUNCTION BASED ON MODELING OF CENSORING PATTERN

    OpenAIRE

    Akio, Suzukawa; Nobuhiro, Taneichi; Department of Animal Production and Agricultural Economics, Obihiro University

    2000-01-01

    The Kaplan-Meier estimator(KM-estimator)is an important tool in the analysis of right censored data. It is a non-parametric estimator of an unknown survival function of a lifetime random variable. The purpose of this paper is to obtain a semi-parametric estimator of the survival function. In many practical data, there are several patterns of censoring, for example, censoring is apt to occur for the larger observable time. Such a pattern can be expressed by a function defined by conditional pr...

  18. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

    Science.gov (United States)

    Yousefi, Safoora; Amrollahi, Fatemeh; Amgad, Mohamed; Dong, Chengliang; Lewis, Joshua E; Song, Congzheng; Gutman, David A; Halani, Sameer H; Velazquez Vega, Jose Enrique; Brat, Daniel J; Cooper, Lee A D

    2017-09-15

    Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

  19. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

    KAUST Repository

    Tekwe, C. D.

    2012-05-24

    MOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: ctekwe@stat.tamu.edu.

  20. A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index

    Directory of Open Access Journals (Sweden)

    Yifei Chen

    2013-01-01

    statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online.

  1. Joint modelling of longitudinal CEA tumour marker progression and survival data on breast cancer

    Science.gov (United States)

    Borges, Ana; Sousa, Inês; Castro, Luis

    2017-06-01

    This work proposes the use of Biostatistics methods to study breast cancer in patients of Braga's Hospital Senology Unit, located in Portugal. The primary motivation is to contribute to the understanding of the progression of breast cancer, within the Portuguese population, using a more complex statistical model assumptions than the traditional analysis that take into account a possible existence of a serial correlation structure within a same subject observations. We aim to infer which risk factors aect the survival of Braga's Hospital patients, diagnosed with breast tumour. Whilst analysing risk factors that aect a tumour markers used on the surveillance of disease progression the Carcinoembryonic antigen (CEA). As survival and longitudinal processes may be associated, it is important to model these two processes together. Hence, a joint modelling of these two processes to infer on the association of these was conducted. A data set of 540 patients, along with 50 variables, was collected from medical records of the Hospital. A joint model approach was used to analyse these data. Two dierent joint models were applied to the same data set, with dierent parameterizations which give dierent interpretations to model parameters. These were used by convenience as the ones implemented in R software. Results from the two models were compared. Results from joint models, showed that the longitudinal CEA values were signicantly associated with the survival probability of these patients. A comparison between parameter estimates obtained in this analysis and previous independent survival[4] and longitudinal analysis[5][6], lead us to conclude that independent analysis brings up bias parameter estimates. Hence, an assumption of association between the two processes in a joint model of breast cancer data is necessary. Results indicate that the longitudinal progression of CEA is signicantly associated with the probability of survival of these patients. Hence, an assumption of

  2. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

    Science.gov (United States)

    Modhukur, Vijayachitra; Iljasenko, Tatjana; Metsalu, Tauno; Lokk, Kaie; Laisk-Podar, Triin; Vilo, Jaak

    2017-12-21

    To develop a web tool for survival analysis based on CpG methylation patterns. We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.

  3. Introduction of a prediction model to assigning periodontal prognosis based on survival rates.

    Science.gov (United States)

    Martinez-Canut, Pedro; Alcaraz, Jaime; Alcaraz, Jaime; Alvarez-Novoa, Pablo; Alvarez-Novoa, Carmen; Marcos, Ana; Noguerol, Blas; Noguerol, Fernando; Zabalegui, Ion

    2017-09-04

    To develop a prediction model for tooth loss due to periodontal disease (TLPD) in patients following periodontal maintenance (PM), and assess its performance using a multicentre approach. A multilevel analysis of eleven predictors of TLPD in 500 patients following PM was carried out to calculate the probability of TLPD. This algorithm was applied to three different TLPD samples (369 teeth) gathered retrospectively by nine periodontist, associating several intervals of probability with the corresponding survival rates, based on significant differences in the mean survival rates. The reproducibility of these associations was assessed in each sample (One-way ANOVA and pair-wise comparison with Bonferroni corrections). The model presented high specificity and moderate sensitivity, with optimal calibration and discrimination measurements. Seven intervals of probability were associated with seven survival rates and these associations contained close to 80% of the cases: the probability predicted the survival rate at this percentage. The model performed well in the three samples, since the mean survival rates of each association were significantly different within each sample, while no significant differences between the samples were found in pair-wise comparisons of means. This model might be useful for predicting survival rates in different TLPD samples This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  4. Modeling association between times to recurrence of the different polarities in bipolar disorder among service seekers in urban Nigeria: a survival analysis approach

    Directory of Open Access Journals (Sweden)

    Fagbamigbe AF

    2017-07-01

    Full Text Available Adeniyi Francis Fagbamigbe,1 Victor Adesola Makanjuola2 1Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria; 2Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan Nigeria Introduction: Bipolar disorder (BD remains both a clinical and public health challenge worldwide, especially in developing countries such as Nigeria. Many studies have focused on prevalence and recurrences among BD service seekers but little has been documented on the nature, strength, direction, existence, and estimation of association between times to recurrence of the two possible polarities or mood episodes in BD. In this study, we explored the association between durations before recurrence of depression and manic episodes among people seeking treatment for BD.Methods: This analytical study used retrospective data of 467 persons who sought treatment for BD at the psychiatric clinic of University College Hospital, Ibadan, Nigeria between 2005 and 2014. Descriptive statistics were used to explore the data. We right-censored the data and obtained Kaplan–Meier estimates of the time to recurrence of the outcomes and transformed the estimates to standardized binormal data using quantile-quantile transformation. The likelihood was maximized to obtain the maximum likelihood estimate of the association parameter at 5% significance level.Results: The mean (± standard deviation age of the respondents was 32.9±12.9 years, this was lower among service seekers who were initially diagnosed with mania than among those initially diagnosed with depression (31.3±11.6, 33.2±11.9, respectively. The median survival time to recurrence of mania and depression among the patients was 1,120 and 745 days, respectively, whereas association between times to recurrence of mania and depression was maximized at 0.67 (95% confidence interval: 0.62–0.71.Conclusion: There exists a strong and positive

  5. Socioeconomic deprivation and cancer survival in Germany: an ecological analysis in 200 districts in Germany.

    Science.gov (United States)

    Jansen, Lina; Eberle, Andrea; Emrich, Katharina; Gondos, Adam; Holleczek, Bernd; Kajüter, Hiltraud; Maier, Werner; Nennecke, Alice; Pritzkuleit, Ron; Brenner, Hermann

    2014-06-15

    Although socioeconomic inequalities in cancer survival have been demonstrated both within and between countries, evidence on the variation of the inequalities over time past diagnosis is sparse. Furthermore, no comprehensive analysis of socioeconomic differences in cancer survival in Germany has been conducted. Therefore, we analyzed variations in cancer survival for patients diagnosed with one of the 25 most common cancer sites in 1997-2006 in ten population-based cancer registries in Germany (covering 32 million inhabitants). Patients were assigned a socioeconomic status according to the district of residence at diagnosis. Period analysis was used to derive 3-month, 5-year and conditional 1-year and 5-year age-standardized relative survival for 2002-2006 for each deprivation quintile in Germany. Relative survival of patients living in the most deprived district was compared to survival of patients living in all other districts by model-based period analysis. For 21 of 25 cancer sites, 5-year relative survival was lower in the most deprived districts than in all other districts combined. The median relative excess risk of death over the 25 cancer sites decreased from 1.24 in the first 3 months to 1.16 in the following 9 months to 1.08 in the following 4 years. Inequalities persisted after adjustment for stage. These major regional socioeconomic inequalities indicate a potential for improving cancer care and survival in Germany. Studies on individual-level patient data with access to treatment information should be conducted to examine the reasons for these socioeconomic inequalities in cancer survival in more detail. © 2013 UICC.

  6. Predicting survival of Salmonella in low-water activity foods: an analysis of literature data.

    Science.gov (United States)

    Santillana Farakos, Sofia M; Schaffner, Donald W; Frank, Joseph F

    2014-09-01

    Factors such as temperature, water activity (aw), substrate, culture media, serotype, and strain influence the survival of Salmonella in low-aw foods. Predictive models for Salmonella survival in low-aw foods at temperatures ranging from 21 to 80(u) C and water activities below 0.6 were previously developed. Literature data on survival of Salmonella in low-aw foods were analyzed in the present study to validate these predictive models and to determine global influencing factors. The results showed the Weibull model provided suitable fits to the data in 75% of the curves as compared with the log-linear model. The secondary models predicting the time required for log-decimal reduction (log δ) and shape factor (log β) values were useful in predicting the survival of Salmonella in low-aw foods. Statistical analysis indicated overall fail-safe secondary models, with 88% of the residuals in the acceptable and safe zones (survival kinetics of Salmonella in low-aw foods and its influencing factors.

  7. Identifying Some Risk Factors for the Time to Death of the Elderly Using the Semi-Parametric Blended Model of Survival Analysis With Competing Risks

    Directory of Open Access Journals (Sweden)

    Samane Hajiabbasi

    2018-01-01

    Conclusion In single-variable fitting, age, history of myocardial infarction, history of stroke, and kidney problems were identified to have significant effects on the time to death of the elderly. Based on one-variable semi-parametric competing risk mixture fitted models, more significant risk factors for the time to death of elderly was identified when compared with a fitted multivariate mode to the data. This implies that the role of some independent variables can be explained by other independent variables.

  8. [Prognostic factors in renal cancer with venous thrombus survival analysis.

    Science.gov (United States)

    Pascual-Fernández, Angela; Calleja-Escudero, Jesús; Gómez de Segura, Cristina; Pesquera-Ortega, Laura; Taylor, James; Fajardo, José Antonio; González de Zárate, Javier; Monllor-Gisbert, Jesús; Cortiñas-González, José Ramón

    2017-07-01

    To analyze surgery for renal cancer with venous thrombus at different levels, perioperative complications and prognostic factors associated to overall, cancer-specific and disease-free survival. Retrospective analysis of 42 cases of renal cancer with venous thrombus performed between 2005 and 2015. The level reached by the thrombus was established according to the Mayo Clinic classification. Postoperative complications were staged according to Clavien-Dindo classification. Most frequent in males. Mean age 65.7 years. 16.6% were tumors with level II thrombus. Subcostal approach was performed in 58.9%. Extracorporeal circulation with cardiac arrest and hypothermia was established in 2 patients. Resection of metastatic disease was performed in 3 patients during radical nephrectomy. Reoperation was 2.3% while, perioperative mortality was 4.7%. 30% presented with metastases at diagnosis. Twenty patients progressed at 15.5 months (3-55). Overall survival was 60 months. The cancer-specific mortality was 75%. Disease-free survival was 30% at 55 months. Surgical treatment of renal cancer with venous thrombus requires a multidisciplinary management. The surgical technique varies according to the level reached by the venous thrombus. Tumor stage is the most important prognostic factor. Thrombus level influences prognosis, with longer survival for patients with tumor thrombus confined to the renal vein (pT3a) in comparison to tumors with thrombus in the atrium (pT3c).

  9. Modeling the Impact of Breast-Feeding by HIV-Infected Women on Child Survival.

    Science.gov (United States)

    Heymann, Sally Jody

    1990-01-01

    Models the survival outcomes of children in developing countries born to women infected with human immunodeficiency virus (HIV) who are breast-fed, bottle-fed, and wet-nursed. Uses decision analysis to assess the relative risk of child mortality from HIV transmission and non-HIV causes associated with different methods of feeding. (FMW)

  10. Application of Cox and Parametric Survival Models to Assess Social Determinants of Health Affecting Three-Year Survival of Breast Cancer Patients.

    Science.gov (United States)

    Mohseny, Maryam; Amanpour, Farzaneh; Mosavi-Jarrahi, Alireza; Jafari, Hossein; Moradi-Joo, Mohammad; Davoudi Monfared, Esmat

    2016-01-01

    Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (±11.9) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

  11. Modeling survival of Listeria monocytogenes in the traditional Greek soft cheese Katiki.

    Science.gov (United States)

    Mataragas, Marios; Stergiou, Virginia; Nychas, George-John E

    2008-09-01

    In the present work, survival of Listeria monocytogenes in the traditional Greek soft, spreadable cheese Katiki was studied throughout the shelf life of the product. Samples of finished cheese were inoculated with a cocktail of five L. monocytogenes strains (ca. 6 log CFU g(-1)) and stored at 5, 10, 15, and 20 degrees C. Acid-stress adaptation or cross-protection to the same stress was also investigated by inoculation of acid-adapted cells in the product. The results showed that pathogen survival was biphasic. Various mathematical equations (Geeraerd, Cerf, Albert-Mafart, Whiting, Zwietering, and Baranyi models) were fitted to the experimental data. A thorough statistical analysis was performed to choose the best model. The Geeraerd model was finally selected, and the results revealed no acid tolerance acquisition (no significant differences, P > 0.05, in the survival rates of the non-acid-adapted and acid-adapted cells). Secondary modeling (second-order polynomial with a(0) = 0.8453, a(1) = -0.0743, and a(2) = 0.0059) of the survival rate (of sensitive population), and other parameters that were similar at all temperatures (fraction of initial population in the major population = 99.98%, survival rate of resistant population = 0.10 day(-1), and initial population = 6.29 log CFU g(-1)), showed that survival of the pathogen was temperature dependent with bacterial cells surviving for a longer period of time at lower temperatures. Finally, the developed predictive model was successfully validated at two independent temperatures (12 and 17 degrees C). This study underlines the usefulness of predictive modeling as a tool for realistic estimation and control of L. monocytogenes risk in food products. Such data are also useful when conducting risk assessment studies.

  12. Survival model construction guided by fit and predictive strength.

    Science.gov (United States)

    Chauvel, Cécile; O'Quigley, John

    2017-06-01

    Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model. These techniques appear to be intuitive. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Measures of predictive strength are used in conjunction with these goodness-of-fit techniques and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. Illustrations from studies in breast cancer show how these tools can be of help in guiding the practical problem of efficient model construction for survival data. © 2016, The International Biometric Society.

  13. Hidden Markov model for dependent mark loss and survival estimation

    Science.gov (United States)

    Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.

    2014-01-01

    Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

  14. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  15. Prognostic survival model for people diagnosed with invasive cutaneous melanoma.

    Science.gov (United States)

    Baade, Peter D; Royston, Patrick; Youl, Philipa H; Weinstock, Martin A; Geller, Alan; Aitken, Joanne F

    2015-01-31

    The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Data from the Queensland Cancer Registry for people (20-89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive lymph nodes was manually extracted from pathology forms. Flexible parametric survival models were combined with multivariable fractional polynomial for selecting variables and transformations of continuous variables. Multiple imputation was used for missing covariate values. The MSI contained the variables thickness (transformed, explained 40.6% of variation in survival), body site (additional 1.9% in variation), metastasis (1.8%), positive nodes (0.7%), ulceration (1.3%), age (1.1%). Royston and Sauerbrei's D statistic (measure of discrimination) was 1.50 (95% CI = 1.44, 1.56) and the corresponding RD2 (measure of explained variation) was 0.47 (0.45, 0.49), demonstrating strong explanatory performance. The Harrell-C statistic was 0.88 (0.88, 0.89). Lacking an external validation dataset, we applied internal-external cross validation to demonstrate the consistency of the prognostic information across geographically-defined subsets of the cohort. The MSI provides good ability to predict survival for melanoma patients. Beyond the immediate clinical use, the MSI may have important public health and research applications for evaluations of public health interventions aimed at reducing deaths from melanoma.

  16. Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

    LENUS (Irish Health Repository)

    Haase, Trutz

    2016-02-29

    A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare.

  17. Estimating true instead of apparent survival using spatial Cormack-Jolly-Seber models

    Science.gov (United States)

    Schaub, Michael; Royle, J. Andrew

    2014-01-01

    Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method.

  18. [Corneal transplant in a second level hospital. A survival analysis].

    Science.gov (United States)

    Hernández-Da Mota, Sergio E; Paniagua Jacobo, Margarita; Gómez Revuelta, Gustavo; Páez Martínez, Raymundo Mauricio

    2013-01-01

    To determine the long-term corneal graft survival in patients of General Hospital Dr. Miguel Silva. This was a retrospective cohort study. Records from patients who underwent corneal transplant surgery at General Hospital Dr. Miguel Silva were analyzed. The percentages of graft failure were obtained. Kaplan-Meier survival analysis was performed to evaluate the long-term cumulative probability of graft non-rejection in all patients according to diagnosis. Overall, 71.9% (CI 95%: 64.8-78.9) of the patients did not have any graft rejections, and 12.5% (CI 95%: 7-18) required a regraft and were considered graft failures. Patients with posttraumatic leucoma had a cumulative probability of non-rejection of 100%. Subjects with keratoconus had a 65% likelihood of non-rejection after 40 months of follow-up. The likelihood of non-rejection was greater than 80% at 100 months of follow-up in pseudophakic bullous keratopathy patients and 60% at 20 months of follow-up in inactive herpetic leucoma patients. Posttraumatic leucoma patients had the greatest cumulative survival probability compared with postherpetic leucoma patients and other patient groups.

  19. Random-effects regression analysis of correlated grouped-time survival data.

    Science.gov (United States)

    Hedeker, D; Siddiqui, O; Hu, F B

    2000-04-01

    Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

  20. Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

    Science.gov (United States)

    Santillana Farakos, Sofia M; Pouillot, Régis; Anderson, Nathan; Johnson, Rhoma; Son, Insook; Van Doren, Jane

    2016-06-16

    Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be

  1. [A new perspective of survival data on clinical epidemiology: introduction of competitive risk model].

    Science.gov (United States)

    Nie, Z Q; Ou, Y Q; Qu, Y J; Yuan, H Y; Liu, X Q

    2017-08-10

    Competing risks occur frequently in the analysis of survival data that should be dealt with competing risk models. Competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Previous commonly used Kaplan-Meier method tends to overestimate the cumulative survival functions, while the traditional Cox proportional hazards model falsely evaluates the effects of covariates on the hazard related to the occurrence of the event. There are few domestic reports mentioning the concept, application and methodology of competing risk model as well as the implementation procedures or resolution of model conditions and parameters. The current work aims to explain the core concept and methodology of the competing risk model and to illustrate the process of analysis on cumulative incidence rate, using both the cause-specific hazard function model and the sub-distribution hazard function model. Software macro code in SAS 9.4 is also provided to assist clinical researchers to further understand the application of the model so to properly analyze the survival data.

  2. Analysis of breath samples for lung cancer survival

    Energy Technology Data Exchange (ETDEWEB)

    Schmekel, Birgitta [Division of of Clinical Physiology, County Council of Östergötland, Linköping (Sweden); Clinical Physiology, Department of Medicine and Health, Faculty of Health Sciences, Linköping University, Linköping (Sweden); Winquist, Fredrik, E-mail: frw@ifm.liu.se [Department of Physics, Chemistry and Biology, Linköping University, Linköping SE-581 83 (Sweden); Vikström, Anders [Department of Pulmonary Medicine, University hospital of Linköping, County Council of Östergötland, Linköping (Sweden)

    2014-08-20

    Graphical abstract: Predictions of survival days for lung cancer patients. - Highlights: • Analyses of exhaled air offer a large diagnostic potential. • Patientswith diagnosed lung cancer were studied using an electronic nose. • Excellent predictions and stable models of survival day were obtained. • Consecutive measurements were very important. - Abstract: Analyses of exhaled air by means of electronic noses offer a large diagnostic potential. Such analyses are non-invasive; samples can also be easily obtained from severely ill patients and repeated within short intervals. Lung cancer is the most deadly malignant tumor worldwide, and monitoring of lung cancer progression is of great importance and may help to decide best therapy. In this report, twenty-two patients with diagnosed lung cancer and ten healthy volunteers were studied using breath samples collected several times at certain intervals and analysed by an electronic nose. The samples were divided into three sub-groups; group d for survivor less than one year, group s for survivor more than a year and group h for the healthy volunteers. Prediction models based on partial least square and artificial neural nets could not classify the collected groups d, s and h, but separated well group d from group h. Using artificial neural net, group d could be separated from group s. Excellent predictions and stable models of survival day for group d were obtained, both based on partial least square and artificial neural nets, with correlation coefficients 0.981 and 0.985, respectively. Finally, the importance of consecutive measurements was shown.

  3. The NEAT Predictive Model for Survival in Patients with Advanced Cancer.

    Science.gov (United States)

    Zucker, Amanda; Tsai, Chiaojung Jillian; Loscalzo, John; Calves, Pedro; Kao, Johnny

    2018-01-24

    We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. From May 2012 to March 2015, 280 consecutive patients with stage IV cancer were prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. The NEAT model evaluated number of active tumors ("N"), Eastern Cooperative Oncology Group (ECOG) performance status ("E"), albumin ("A") and primary tumor site ("T"). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logistical regression were utilized to compare model performance. Number of active tumors, performance status, albumin, primary tumor site, prior hospitalization within the last 3 months and liver metastases predicted overall survival on uinvariate and multivariable analysis (pNEAT model separated patients into 4 prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (pNEAT model had a C-index of 0.76 with a Nagelkerke's R2 of 0.54 suggesting good discrimination, calibration and total performance. The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer.

  4. Evaluation of different approaches for modeling Escherichia coli O157:H7 survival on field lettuce.

    Science.gov (United States)

    McKellar, Robin C; Peréz-Rodríguez, Fernando; Harris, Linda J; Moyne, Anne-Laure; Blais, Burton; Topp, Ed; Bezanson, Greg; Bach, Susan; Delaquis, Pascal

    2014-08-01

    The ability to predict the behavior of Escherichia coli O157:H7 on contaminated field lettuce is essential for the development of accurate quantitative microbial risk assessments. The survival pattern of the species was assessed from several data sets derived from field-based experiments, which were analyzed by regression analysis fitting one monophasic model (log-linear) and two biphasic (Weibull and Cerf's model) models. Probabilistic models were also simulated with @RISK™, integrating the fitted monophasic and biphasic models in order to analyze their impact on the estimate of the extent of die-off subsequent to a contamination event in the field. Regression analysis indicated that E. coli O157:H7 followed a biphasic decay pattern in most cases, with the Weibull and Cerf's model showing similar good fit to individual and pooled survival data. Furthermore, results from the stochastic analysis demonstrated that using the log-linear model could lead to different risk estimates from those obtained with biphasic models, with a lower prevalence in the former scenario as no tailing is assumed in this model. The models and results derived from this work provide the first suitable mathematical base upon which to build probabilistic models to predict the fate of E. coli O157:H7 on field-grown leafy green vegetable. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

  5. A Paired Kidney Analysis of Multiorgan Transplantation: Implications for Allograft Survival.

    Science.gov (United States)

    Choudhury, Rashikh A; Reese, Peter P; Goldberg, David S; Bloom, Roy D; Sawinski, Deirdre L; Abt, Peter L

    2017-02-01

    United Network for Organ Sharing multiorgan transplantation allocation policy allows sequestration of a kidney by another solid organ regardless of the priority of the candidate for the kidney allograft. The implications of this policy for kidney allograft survival are not well understood. We conducted a retrospective cohort analysis of pairs of deceased donor kidney transplants where 1 kidney was allocated to a simultaneous liver-kidney (SLK) or simultaneous heart-kidney (SHK) recipient and the contralateral kidney to a kidney transplant alone (KTA) recipient (cohort from February 2002 to December 2010). Graft and patient survivals were assessed with Cox regression models. There were 1998 SLK and 276 SHK transplants with matching KTA transplants. Five-year kidney graft (64% [SLK] vs 75% [KTA], P transplant was 115 years, and by 5 years, the difference increased to 1062 years. Among the SHK arm of our study, 5-year graft survival (72% [SHK] vs 73% [KTA], P = 0.71) did not significantly differ, although patient survival (75% [SHK] vs 84% [KTA], P = 0.02) was higher in KTA recipients. Kidney graft survival is inferior among SLK relative to KTA, but not SHK. Multiorgan transplantation allocation may not be congruent with the intention of new kidney allocation policies that attempt to maximize survival after kidney transplantation.

  6. Bayesian survival analysis in clinical trials: What methods are used in practice?

    Science.gov (United States)

    Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

    2017-02-01

    Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment

  7. Modelling human myoblasts survival upon xenotransplantation into immunodeficient mouse muscle.

    Science.gov (United States)

    Praud, Christophe; Vauchez, Karine; Zongo, Pascal; Vilquin, Jean-Thomas

    2018-03-15

    Cell transplantation has been challenged in several clinical indications of genetic or acquired muscular diseases, but therapeutic success were mitigated. To understand and improve the yields of tissue regeneration, we aimed at modelling the fate of CD56-positive human myoblasts after transplantation. Using immunodeficient severe combined immunodeficiency (SCID) mice as recipients, we assessed the survival, integration and satellite cell niche occupancy of human myoblasts by a triple immunohistochemical labelling of laminin, dystrophin and human lamin A/C. The counts were integrated into a classical mathematical decline equation. After injection, human cells were essentially located in the endomysium, then they disappeared progressively from D0 to D28. The final number of integrated human nuclei was grossly determined at D2 after injection, suggesting that no more efficient fusion between donor myoblasts and host fibers occurs after the resolution of the local damages created by needle insertion. Almost 1% of implanted human cells occupied a satellite-like cell niche. Our mathematical model validated by histological counting provided a reliable quantitative estimate of human myoblast survival and/or incorporation into SCID muscle fibers. Informations brought by histological labelling and this mathematical model are complementary. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. [Epidemiological analysis of leukemia survival in Cracow for cases registered in 1980-1990].

    Science.gov (United States)

    Fornal, Maria; Janicki, Kazimierz; Grodzicki, Tomasz

    2003-01-01

    The aim of the study was epidemiological analysis of survival from all types of leukemia occurring in Cracow in the years 1980-1990. The study was focused on survival times in patients according to a) cytologico-clinical type of leukemia, b) timeframe in which treatment was initiated (between 1980-1985 and 1986-1090). All patients diagnosed of leukemia between the years 1980-1990, living in Cracow and whose cytologico-clinical picture was determined had their survival times and censored survival times established. Survival until 1997 was taken into account. For each cytologico-clinical type of leukemia survival function according to Kaplan-Meier was calculated. The Cox model was implemented to analyze the risk of death depending on the period in which the disease appeared--two time frames were established 1980-1985 and 1986-1990. Other parameters considered were; age, sex and area in which the patient lived (suburb). Practically in all types of leukemia a higher probability of survival was found in patients in whom leukemia was diagnosed (and consequently treated) in the second period i.e., 1986-1990. The highest achievement was observed in acute lymphoblastic leukemia in children, in which the relative 5-year survival probability rose from 35% in the years 1980-1985 to 78% in the years 1986-1990, thus achieving the level of well developed countries. A similar picture was seen in chronic lymphocytic leukemia where the relative 5 year survival probability rose from 57% to 77%, and in chronic granulocytic leukemia where the 5 year survival probabilities were accordingly 23% and 39%. All cited values for the second period of analysis are at the levels noted in the United States and in Europe. The positive changes in the survival times observed in patients with leukemia seen in the second half of the 80-ies (in comparison to the period 1980-1985) has been interpreted as the result of advancements in therapy of the disease in Cracow.

  9. Rurality and survival differences in lung cancer: a large population-based multivariate analysis.

    Science.gov (United States)

    Pozet, Astrid; Westeel, Virginie; Berion, Pascal; Danzon, Arlette; Debieuvre, Didier; Breton, Jean-Luc; Monnier, Alain; Lahourcade, Jean; Dalphin, Jean-Charles; Mercier, Mariette

    2008-03-01

    Several studies have suggested rural health disadvantages. In France, studies on rural-urban patterns of lung cancer survival have yielded conflicting results. The aim of this analysis was to determine whether rural residence was associated with poor survival in three French counties. The database consisted of all primary lung cancer cases diagnosed in 2000 and 2001 collected through the Doubs cancer registry. A degree of rurality, obtained from socio-demographic and farming parameters of the 1999 French census treated with factor analysis, was attributed to each patient according to his/her place of residence. Among the 802 patients, 21% resided in rural areas, 11% were semi-urban inhabitants and 68% were urban residents. Survival differed significantly between these three rurality categories (p=0.04), with 2-year survival rates of 18, 29 and 24%, respectively. Using a Cox model, rural areas were significantly correlated with poor survival as compared with semi-urban areas (OR=1.42; 95% confidence interval=1.06-1.90; p=0.02). There was no survival difference between semi-urban and urban patients (OR=1.18; 95% confidence interval=0.91-1.53; p=0.21). Patient and tumour characteristics, especially stage and staging procedures, as well as first line treatment, did not vary with the degree of rurality. In conclusion, rurality has to be considered as a strong prognostic factor. Several intricate factors might be hypothesized such as increasing time to diagnosis leading to heavier tumour burden, worse treatment compliance and socioeconomic status. Before practical interventions can be proposed, prospective studies are warranted with further definition of rural risk factors for decreased survival in rural lung cancer patients.

  10. Effect of birth spacing on infant survival in Thailand: two-stage logit analysis.

    Science.gov (United States)

    Park, C B; Siasakul, S; Saengtienchai, C

    1994-03-01

    We formulated a two-stage causal model for infant survival and applied it to data drawn from the 1987 Thai Demographic and Health Survey covering the fate of 5,074 index children. The following six variables were considered as the explanatory variables: maternal age, maternal education, birth order, preceding birth interval, survival of the preceding child, and place of residence. The analysis suggests that the birth interval not only directly affected the chance of infant survival but it played the role of the filtering factor through which other variables indirectly operate on infant mortality. The effect of preceding child's death was very strong, the odds ratios for the following infant's death and short birth interval both exceeding three.

  11. Computer based prognosis model with dimensionality reduction and validation of attributes for prolonged survival prediction

    Directory of Open Access Journals (Sweden)

    C.G. Raji

    2017-01-01

    Full Text Available Medical databases contain large volume of data about patients and their clinical information. For extracting the features and their relationships from a huge database, various data mining techniques need to be employed. As Liver transplantation is the curative surgical procedure for the patients suffering from end stage liver disease, predicting the survival rate after Liver transplantation has a big impact. Appropriate selection of attributes and methods are necessary for the survival prediction. Liver transplantation data with 256 attributes were collected from 389 attributes of the United Nations Organ Sharing registry for the survival prediction. Initially 59 attributes were filtered manually, and then Principal Component Analysis (PCA was applied for reducing the dimensionality of the data. After performing PCA, 197 attributes were obtained and they were ranked into 27 strong/relevant attributes. Using association rule mining techniques, the association between the selected attributes was identified and verified. Comparison of rules generated by various association rules mining algorithm before and after PCA was also carried out for affirming the results. The various rule mining algorithms used were Apriori, Treap mining and Tertius algorithms. Among these algorithms, Treap mining algorithm generated the rules with high accuracy. A Multilayer Perceptron model was built for predicting the long term survival of patients after Liver transplantation which produced high accuracy prediction result. The model performance was compared with Radial Basis Function model to prove the accuracy of survival of liver patients'. The top ranked attributes obtained from rule mining were fed to the models for effective training. This ensures that Treap mining generated associations of high impact attributes which in-turn made the survival prediction flawless.

  12. Survival analysis of irish amyotrophic lateral sclerosis patients diagnosed from 1995-2010.

    Directory of Open Access Journals (Sweden)

    James Rooney

    Full Text Available INTRODUCTION: The Irish ALS register is a valuable resource for examining survival factors in Irish ALS patients. Cox regression has become the default tool for survival analysis, but recently new classes of flexible parametric survival analysis tools known as Royston-Parmar models have become available. METHODS: We employed Cox proportional hazards and Royston-Parmar flexible parametric modeling to examine factors affecting survival in Irish ALS patients. We further examined the effect of choice of timescale on Cox models and the proportional hazards assumption, and extended both Cox and Royston-Parmar models with time varying components. RESULTS: On comparison of models we chose a Royston-Parmar proportional hazards model without time varying covariates as the best fit. Using this model we confirmed the association of known survival markers in ALS including age at diagnosis (Hazard Ratio (HR 1.34 per 10 year increase; 95% CI 1.26-1.42, diagnostic delay (HR 0.96 per 12 weeks delay; 95% CI 0.94-0.97, Definite ALS (HR 1.47 95% CI 1.17-1.84, bulbar onset disease (HR 1.58 95% CI 1.33-1.87, riluzole use (HR 0.72 95% CI 0.61-0.85 and attendance at an ALS clinic (HR 0.74 95% CI 0.64-0.86. DISCUSSION: Our analysis explored the strengths and weaknesses of Cox proportional hazard and Royston-Parmar flexible parametric methods. By including time varying components we were able to gain deeper understanding of the dataset. Variation in survival between time periods appears to be due to missing data in the first time period. The use of age as timescale to account for confounding by age resolved breaches of the proportional hazards assumption, but in doing so may have obscured deficiencies in the data. Our study demonstrates the need to test for, and fully explore, breaches of the Cox proportional hazards assumption. Royston-Parmar flexible parametric modeling proved a powerful method for achieving this.

  13. Statistical modelling of survival data with random effects h-likelihood approach

    CERN Document Server

    Ha, Il Do; Lee, Youngjo

    2017-01-01

    This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to research...

  14. Factors Influencing the Cure Rate in the Corneal Graft Rejection with Survival Analysis

    Directory of Open Access Journals (Sweden)

    Feizi S.

    2009-11-01

    Full Text Available AbstractBackground and Objectives: Immunologic rejection of the transplanted cornea is the major cause of human allograft failure with several risk factors contributing to it. Since in the corneal graft, most individuals do not reject the graft, we used the survival analysis with cure rate for the assessment of the factors influencing the cure rate at the time of data analysis. The main aim of this study was to evaluate the cure rate and assess the risk factors for corneal graft rejection in the keratoconus disease in Labafinejad Hospital, Tehran, Iran. Methods: This was a routine data base study in which the data were gathered from keratoconus patients’ files that had undergone penetrating keratoplasty operation. In the survival analysis, individuals who didn’t reject corneal were considered cured. To study the factors influencing the cure rate, we used the Weibull distribution for survival function and the logistic link function for the cure rate because of their tractability and accuracy.Results: Out of 119 patients 31 patients (26% rejected grafts. Among the factors influencing cure rate, only in vascularization and in persons older than 25 years of age was ameaningful effect on decreasing cure rate. With this cure model, the expected cure rate in the non-vascularization and less than 25 year- old patients was 81, in non-vascularization and more than 25 year- olds it is 64, in the vascularization and less than 25 year- olds, the cure rate is 19 and in the vascularization and more than 25 years of age, the cure rate is 9 percent and the observed cure rate for Kaplan-Meier product limit estimator was 79, 61, 27 and 0 percent, respectively. The results showed that the estimate of cure rate in the survival analysis was near the Kaplan-Meier product-limits estimator.Conclusion: One of the benefits of modeling is its ability to generalize the results; using them in the prediction. According to the results obtained from the fitting cure model

  15. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

    National Research Council Canada - National Science Library

    Guyot, Patricia; Ades, A E; Ouwens, Mario J N M; Welton, Nicky J

    2012-01-01

    .... In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient...

  16. Multimodality treatment of brain metastases: an institutional survival analysis of 275 patients

    Directory of Open Access Journals (Sweden)

    Demakas John J

    2011-07-01

    Full Text Available Abstract Background Whole brain radiation therapy (WBRT, surgical resection, stereotactic radiosurgery (SRS, and combinations of the three modalities are used in the management of patients with metastatic brain tumors. We present the previously unreported survival outcomes of 275 patients treated for newly diagnosed brain metastases at Cancer Care Northwest and Gamma Knife of Spokane between 1998 and 2008. Methods The effects treatment regimen, age, Eastern Cooperative Oncology Group-Performance Status (ECOG-PS, primary tumor histology, number of brain metastases, and total volume of brain metastases have on patient overall survival were analyzed. Statistical analysis was performed using Kaplan-Meier survival curves, Andersen 95% confidence intervals, approximate confidence intervals for log hazard-ratios, and multivariate Cox proportional hazard models. Results The median clinical follow up time was 7.2 months. On multivariate analysis, survival statistically favored patients treated with SRS alone when compared to patients treated with WBRT alone (p Conclusions In our analysis, patients benefited from a combined modality treatment approach and physicians must consider patient age, performance status, and primary tumor histology when recommending specific treatments regimens.

  17. BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.

    Science.gov (United States)

    Sinha, Arijit; Chi, Zhiyi; Chen, Ming-Hui

    2015-10-01

    Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.

  18. Small sample bias in the gamma frailty model for univariate survival.

    Science.gov (United States)

    Barker, Peter; Henderson, Robin

    2005-06-01

    The gamma frailty model is a natural extension of the Cox proportional hazards model in survival analysis. Because the frailties are unobserved, an E-M approach is often used for estimation. Such an approach is shown to lead to finite sample underestimation of the frailty variance, with the corresponding regression parameters also being underestimated as a result. For the univariate case, we investigate the source of the bias with simulation studies and a complete enumeration. The rank-based E-M approach, we note, only identifies frailty through the order in which failures occur; additional frailty which is evident in the survival times is ignored, and as a result the frailty variance is underestimated. An adaption of the standard E-M approach is suggested, whereby the non-parametric Breslow estimate is replaced by a local likelihood formulation for the baseline hazard which allows the survival times themselves to enter the model. Simulations demonstrate that this approach substantially reduces the bias, even at small sample sizes. The method developed is applied to survival data from the North West Regional Leukaemia Register.

  19. Models of Economic Analysis

    OpenAIRE

    Adrian Ioana; Tiberiu Socaciu

    2013-01-01

    The article presents specific aspects of management and models for economic analysis. Thus, we present the main types of economic analysis: statistical analysis, dynamic analysis, static analysis, mathematical analysis, psychological analysis. Also we present the main object of the analysis: the technological activity analysis of a company, the analysis of the production costs, the economic activity analysis of a company, the analysis of equipment, the analysis of labor productivity, the anal...

  20. Semi-parametric regression model for survival data: graphical visualization with R.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-12-01

    Cox proportional hazards model is a semi-parametric model that leaves its baseline hazard function unspecified. The rationale to use Cox proportional hazards model is that (I) the underlying form of hazard function is stringent and unrealistic, and (II) researchers are only interested in estimation of how the hazard changes with covariate (relative hazard). Cox regression model can be easily fit with coxph() function in survival package. Stratified Cox model may be used for covariate that violates the proportional hazards assumption. The relative importance of covariates in population can be examined with the rankhazard package in R. Hazard ratio curves for continuous covariates can be visualized using smoothHR package. This curve helps to better understand the effects that each continuous covariate has on the outcome. Population attributable fraction is a classic quantity in epidemiology to evaluate the impact of risk factor on the occurrence of event in the population. In survival analysis, the adjusted/unadjusted attributable fraction can be plotted against survival time to obtain attributable fraction function.

  1. Estimating Probability of Default on Peer to Peer Market – Survival Analysis Approach

    Directory of Open Access Journals (Sweden)

    Đurović Andrija

    2017-05-01

    Full Text Available Arguably a cornerstone of credit risk modelling is the probability of default. This article aims is to search for the evidence of relationship between loan characteristics and probability of default on peer-to-peer (P2P market. In line with that, two loan characteristics are analysed: 1 loan term length and 2 loan purpose. The analysis is conducted using survival analysis approach within the vintage framework. Firstly, 12 months probability of default through the cycle is used to compare riskiness of analysed loan characteristics. Secondly, log-rank test is employed in order to compare complete survival period of cohorts. Findings of the paper suggest that there is clear evidence of relationship between analysed loan characteristics and probability of default. Longer term loans are more risky than the shorter term ones and the least risky loans are those used for credit card payoff.

  2. Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

    Directory of Open Access Journals (Sweden)

    Troy W Grovenburg

    Full Text Available New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old ungulates: 76 white-tailed deer (Odocoileus virginianus in Minnesota and South Dakota, 61 mule deer (O. hemionus in California, and 37 pronghorn (Antilocapra americana in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i

  3. Models of Economic Analysis

    Directory of Open Access Journals (Sweden)

    Adrian Ioana

    2013-07-01

    Full Text Available The article presents specific aspects of management and models for economic analysis. Thus, we present the main types of economic analysis: statistical analysis, dynamic analysis, static analysis, mathematical analysis, psychological analysis. Also we present the main object of the analysis: the technological activity analysis of a company, the analysis of the production costs, the economic activity analysis of a company, the analysis of equipment, the analysis of labor productivity, the analysis of the goods flow.Keywords: Economic analysis, Models, Adoption of innovation

  4. Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis

    NARCIS (Netherlands)

    Neerhof, H.J.; Madsen, P.; Ducrucq, V.; Vollema, A.R.; Jensen, I.; Korsgaard, I.R.

    2000-01-01

    The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis

  5. External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

    Science.gov (United States)

    Guardiola, Jordi; Baliellas, Carme; Xiol, Xavier; Fernandez Esparrach, Glòria; Ginès, Pere; Ventura, Pere; Vazquez, Santiago

    2002-09-01

    Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

  6. Survival analysis of HIV-infected patients under antiretroviral ...

    African Journals Online (AJOL)

    admin

    Abstract. Background: The introduction of ART dramatically improved the survival and health quality of HIV-infected patients in the industrialized world; and the survival benefit of ART has been well studied too. However, in resource-poor settings, where such treatment was started only recently, limited data exist on treatment ...

  7. Lung cancer associated hypercalcemia: An analysis of factors influencing survival and prognosis in 34 cases

    Directory of Open Access Journals (Sweden)

    Su-jie ZHANG

    2012-06-01

    Full Text Available Objectives  To explore the factors influencing survival time in lung cancer associated hypercalcemia patients. Methods  Thirty-four patients with pathologically confirmed lung cancer complicated with hypercalcemia, who were treated at the Department of Oncology in General Hospital of PLA from Jan. 2001 to Dec. 2010, were enrolled in this study. The clinical data analyzed included sex, age, pathological type of the malignancies, organ metastasis (bone, lung, liver, kidney, brain, number of distal metastatic site, mental status, interval between final diagnosis of lung cancer and of hypercalcemia, peak value of blood calcium during the disease course, treatment methods and so on. Survival analysis was performed with the Kaplan-Meier method and Cox analysis with statistic software SPSS 18.0 to identify the potential prognostic factors. Results  The highest blood calcium level ranged from 2.77 to 4.87mmol/L, and the median value was 2.94mmol/L. The patients' survival time after diagnosis of hypercalcemia varied from 1 day to 1067 days, and the median survival time was 92 days. With the log-rank test, age above 50 years old, hypercalcemia occurring over 90 days after diagnosis of cancer, central nervous system symptoms and renal metastasis were predictors for poor survival (P=0.048, P=0.001, P=0.000, P=0.003. In the COX proportional hazard model analysis, age above 50 years old, hypercalcemia occurring over 90 days after cancer diagnosis, central nervous system symptoms and renal metastasis were significant prognostic factors for poor survival (HR=11.483, P=0.006; HR=4.371, P=0.002; HR=6.064, P=0.026; HR=8.502, P=0.011. Conclusions  Patients with lung cancer associated hypercalcemia have a shorter survival time and poor prognosis. Age above 50 years old, hypercalcemia occurring over 90 days after cancer diagnosis, central nervous system symptoms and renal metastasis are significant factors of poor prognosis.

  8. Modeling post-fledging survival of lark buntings in response to ecological and biological factors

    Science.gov (United States)

    Yackel Adams, A.A.; Skagen, S.K.; Savidge, J.A.

    2006-01-01

    We evaluated the influences of several ecological, biological, and methodological factors on post-fledging survival of a shortgrass prairie bird, the Lark Bunting (Calamospiza melanocorys). We estimated daily post-fledging survival (n = 206, 82 broods) using radiotelemetry and color bands to track fledglings. Daily survival probabilities were best explained by drought intensity, time in season (quadratic trend), ages ≤3 d post-fledging, and rank given drought intensity. Drought intensity had a strong negative effect on survival. Rank was an important predictor of fledgling survival only during the severe drought of 2002 when the smallest fledglings had lower survival. Recently fledged young (ages ≤3 d post-fledging) undergoing the transition from nest to surrounding habitat experienced markedly lower survival, demonstrating the vulnerable nature of this time period. Survival was greater in mid and late season than early season, corresponding to our assumptions of food availability. Neither mark type nor sex of attending parent influenced survival. The model-averaged product of the 22-d survival calculated using mean rank and median value of time in season was 0.360 ± 0.08 in 2001 and 0.276 ± 0.08 in 2002. Survival estimates that account for age, condition of young, ecological conditions, and other factors are important for parameterization of realistic population models. Biologists using population growth models to elucidate mechanisms of population declines should attempt to estimate species-specific of post-fledging survival rather than use generalized estimates.

  9. Factors relating to poor survival rates of aged cervical cancer patients: a population-based study with the relative survival model in Osaka, Japan.

    Science.gov (United States)

    Ioka, Akiko; Ito, Yuri; Tsukuma, Hideaki

    2009-01-01

    Poor survival of older cervical cancer patients has been reported; however, related factors, such as the extent of disease and the competitive risk by aging have not been well evaluated. We applied the relative survival model developed by Dickman et al to resolve this issue. Study subjects were cervical cancer patients retrieved from the Osaka Cancer Registry. They were limited to the 10,048 reported cases diagnosed from 1975 to 1999, based on the quality of data collection on vital status. Age at diagnosis was categorized into or = 65 years. The impact of prognostic factors on 5-year survival was evaluated with the relative survival model, incorporating patients' expected survival in multivariate analysis. The age-specific relative excess risk (RER) of death was significantly higher for older groups as compared with women aged 30-54 years (RER, 1.58 at 55-64 and 2.51 at > or = 65 years). The RER was decreased by 64.8% among the 55-64 year olds as an effect of cancer stage at diagnosis, and by 43.4% among those 65 years old and over. After adding adjustment for treatment modalities, the RER was no longer significantly higher among 55-64 year olds; however, it was still higher among 65 year olds and over. Advanced stage at diagnosis was the main determinant of poor survival among the aged cervical cancer patients, although other factors such as limitations on the combination of treatment were also suggested to have an influence in those aged 65 years and over.

  10. Survival Analysis of Breast Cancer Subtypes in Patients With Spinal Metastases

    DEFF Research Database (Denmark)

    Wang, Miao; Jensen, Anders Bonde; Morgen, Soeren Smith

    2014-01-01

    hazards regression model unadjusted and adjusted by age were used. RESULTS: Patients with ER-negative (-) breast cancer had 11 months shorter median survival duration (10.6 vs. 21.5 mo) and 48% higher mortality risk (P=0.03) than those with ER-positive (+) breast cancer. Patients with PgR (-) status had...... in determining breast cancer subtypes and predicting patients' response to adjuvant treatments. METHODS: Until August 2013, we retrieved 151 surgically treated patients with breast cancer spinal metastases and followed up all the patients for at least 2 years. Survival duration analysis and Cox proportional...... from score "5" to "3" in Tokuhashi scoring system and from "slow growth" to "moderate growth" in Tomita scoring system. Spine surgeons should be critical before performing high-risk extensive surgery in patients with ER/HR (-) status, and especially, in those with triple-negative status. LEVEL...

  11. A prognostic model of therapy-related myelodysplastic syndrome for predicting survival and transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Daver, Naval; Kim, Hawk; Dinardo, Courtney; Jabbour, Elias; Kadia, Tapan; Borthakur, Gautam; Pierce, Sherry; Shan, Jianqin; Cardenas-Turanzas, Marylou; Cortes, Jorge; Ravandi, Farhad; Wierda, William; Estrov, Zeev; Faderl, Stefan; Wei, Yue; Kantarjian, Hagop; Garcia-Manero, Guillermo

    2014-10-01

    We evaluated the characteristics of a cohort of patients with myelodysplastic syndrome (MDS) related to therapy (t-MDS) to create a prognostic model. We identified 281 patients with MDS who had received previous chemotherapy and/or radiotherapy for previous malignancy. Potential prognostic factors were determined using univariate and multivariate analyses. Multivariate Cox regression analysis identified 7 factors that independently predicted short survival in t-MDS: age ≥ 65 years (hazard ratio [HR], 1.63), Eastern Cooperative Oncology Group performance status 2-4 (HR, 1.86), poor cytogenetics (-7 and/or complex; HR, 2.47), World Health Organization MDS subtype (RARs or RAEB-1/2; HR, 1.92), hemoglobin (HR, 2.24), platelets (HR, 2.01), and transfusion dependency (HR, 1.59). These risk factors were used to create a prognostic model that segregated patients into 3 groups with distinct median overall survival: good (0-2 risk factors; 34 months), intermediate (3-4 risk factors; 12 months), and poor (5-7 risk factors; 5 months) (P < .001) and 1-year leukemia-free survival (96%, 84%, and 72%, respectively, P = .003). This model also identified distinct survival groups according to t-MDS therapy. In summary, we devised a prognostic model specifically for patients with t-MDS that predicted overall survival and leukemia-free survival. This model might facilitate the development of risk-adapted therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Study of Hip Fracture Risk using Tree Structured Survival Analysis

    Directory of Open Access Journals (Sweden)

    Lu Y

    2003-01-01

    Full Text Available In dieser Studie wird das Hüftfraktur-Risiko bei postmenopausalen Frauen untersucht, indem die Frauen in verschiedene Subgruppen hinsichtlich dieses Risikos klassifiziert werden. Frauen in einer gemeinsamen Subgruppe haben ein ähnliches Risiko, hingegen in verschiedenen Subgruppen ein unterschiedliches Hüftfraktur-Risiko. Die Subgruppen wurden mittels der Tree Structured Survival Analysis (TSSA aus den Daten von 7.665 Frauen der SOF (Study of Osteoporosis Fracture ermittelt. Bei allen Studienteilnehmerinnen wurde die Knochenmineraldichte (BMD von Unterarm, Oberschenkelhals, Hüfte und Wirbelsäule gemessen. Die Zeit von der BMD-Messung bis zur Hüftfraktur wurde als Endpunkt notiert. Eine Stichprobe von 75% der Teilnehmerinnen wurde verwendet, um die prognostischen Subgruppen zu bilden (Trainings-Datensatz, während die anderen 25% als Bestätigung der Ergebnisse diente (Validierungs-Datensatz. Aufgrund des Trainings-Datensatzes konnten mittels TSSA 4 Subgruppen identifiziert werden, deren Hüftfraktur-Risiko bei einem Follow-up von im Mittel 6,5 Jahren bei 19%, 9%, 4% und 1% lag. Die Einteilung in die Subgruppen erfolgte aufgrund der Bewertung der BMD des Ward'schen Dreiecks sowie des Oberschenkelhalses und nach dem Alter. Diese Ergebnisse konnten mittels des Validierungs-Datensatzes reproduziert werden, was die Sinnhaftigkeit der Klassifizierungregeln in einem klinischen Setting bestätigte. Mittels TSSA war eine sinnvolle, aussagekräftige und reproduzierbare Identifikation von prognostischen Subgruppen, die auf dem Alter und den BMD-Werten beruhen, möglich. In this paper we studied the risk of hip fracture for post-menopausal women by classifying women into different subgroups based on their risk of hip fracture. The subgroups were generated such that all the women in a particular subgroup had relatively similar risk while women belonging to two different subgroups had rather different risks of hip fracture. We used the Tree Structured

  13. SURVIVAL ANALYSIS OF CANCER PATIENTS USING PARAMETRIC AND NON-PARAMETRIC APPROACHES

    Directory of Open Access Journals (Sweden)

    M. AKRAM, M. AMAN ULLAH AND R. TAJ

    2007-10-01

    Full Text Available Exploring the health related quality of life is usually the focus of the survival studies. Using the health data of cancer registry in Multan, Pakistan, an investigation about the survival pattern of cancer patients was explored, using the non-parametric and parametric modeling strategies. The Kaplan-Meier method and Weibull model based on Anderson-Darling test were applied to the real life time data. Findings suggested different sex-superiority of survival pattern among different groups of cancer patients. Interestingly, Kaplan-Meier and Weibul model provided a very close estimate of the survival function and other characteristics of interest.

  14. Meta-analysis of the effects of beta blocker on survival time in cancer patients.

    Science.gov (United States)

    Choi, Chel Hun; Song, Taejong; Kim, Tae Hyun; Choi, Jun Kuk; Park, Jin-Young; Yoon, Aera; Lee, Yoo-Young; Kim, Tae-Joong; Bae, Duk-Soo; Lee, Jeong-Won; Kim, Byoung-Gie

    2014-07-01

    This study was to elucidate the potential benefit of beta blockers on cancer survival. We comprehensively searched PubMed, Embase, and the Cochrane Library from their inception to April 2013. Two authors independently screened and reviewed the eligibility of each study and coded the participants, treatment, and outcome characteristics. The primary outcomes were overall survival (OS) and disease-free survival (DFS). Twelve studies published between 1993 and 2013 were included in the final analysis. Four papers reported results from 10 independent groups, resulting in a total of 18 comparisons based on data obtained from 20,898 subjects. Effect sizes (hazard ratios, HR) were heterogeneous, and random-effects models were used in the analyses. The meta-analysis demonstrated that beta blocker use is associated with improved OS (HR 0.79; 95 % CI 0.67-0.93; p = 0.004) and DFS (HR 0.69; 95 % CI 0.53-0.91; p = 0.009). Although statistically not significant, the effect size was greater in patients with low-stage cancer or cancer treated primarily with surgery than in patients with high-stage cancer or cancer treated primarily without surgery (HR 0.60 vs. 0.78, and 0.60 vs. 0.80, respectively). Although only two study codes were analyzed, the studies using nonselective beta blockers showed that there was no overall effect on OS (HR 0.52, 95 % CI 0.09-3.04). This meta-analysis provides evidence that beta blocker use can be associated with the prolonged survival of cancer patients, especially patients with early-stage cancer treated primarily with surgery.

  15. Young patients with colorectal cancer have poor survival in the first twenty months after operation and predictable survival in the medium and long-term: Analysis of survival and prognostic markers

    Directory of Open Access Journals (Sweden)

    Wickramarachchi RE

    2010-09-01

    Full Text Available Abstract Objectives This study compares clinico-pathological features in young (50 years with colorectal cancer, survival in the young and the influence of pre-operative clinical and histological factors on survival. Materials and methods A twelve year prospective database of colorectal cancer was analysed. Fifty-three young patients were compared with forty seven consecutive older patients over fifty years old. An analysis of survival was undertaken in young patients using Kaplan Meier graphs, non parametric methods, Cox's Proportional Hazard Ratios and Weibull Hazard models. Results Young patients comprised 13.4 percent of 397 with colorectal cancer. Duration of symptoms and presentation in the young was similar to older patients (median, range; young patients; 6 months, 2 weeks to 2 years, older patients; 4 months, 4 weeks to 3 years, p > 0.05. In both groups, the majority presented without bowel obstruction (young - 81%, older - 94%. Cancer proximal to the splenic flexure was present more in young than in older patients. Synchronous cancers were found exclusively in the young. Mucinous tumours were seen in 16% of young and 4% of older patients (p Conclusion If patients, who are less than 40 years old with colorectal cancer, survive twenty months after operation, the prognosis improves and their survival becomes predictable.

  16. Demographic analysis of dormancy and survival in the terrestrial orchid Cypripedium reginae

    Science.gov (United States)

    Kery, Marc; Gregg, Katharine B.

    2004-01-01

    1. We use capture-recapture models to estimate the fraction of dormant ramets, survival and state transition rates, and to identify factors affecting these rates, for the terrestrial orchid Cypripedium reginae. We studied two populations in West Virginia, USA, for 11 years and investigated relationships between grazing and demography. Abe Run's population was small, with moderate herbivory by deer and relatively constant population size. The population at Big Draft was of medium size, with heavy deer grazing, and a sharply declining number of flowering plants up to the spring before our study started, when the population was fenced. 2. We observed dormant episodes lasting from 1 to 4 years. At Abe Run and Big Draft, 32.5% and 7.4% of ramets, respectively, were dormant at least once during the study period for an average of 1.6 and 1.3 years, respectively. We estimated the annual fraction of ramets in the dormant state at 12.3% (95% CI 9.5-15.8%) at Abe Run and at 1.8% (95% CI 1.2-2.6%) at Big Draft. Transition rates between the dormant, vegetative and flowering life-states did not vary between years in either population. Most surviving ramets remained in the same state from one year to the next. Survival rates were constant at Abe Run (0.96, 95% CI 0.93-0.97), but varied between years at Big Draft (0.89-0.99, mean 0.95). 3. At Big Draft, we found neither a temporal trend in survival after cessation of grazing, nor relationships between survival and the number of spring frost days or cumulative precipitation during the current or the previous 12 months. However, analysis of precipitation on a 3-month basis revealed a positive relationship between survival and precipitation during the spring (March-May) of the previous year. 4. Relationship between climate and the population dynamics of orchids may have to be studied with a fine temporal resolution, and considering possible time lags. Capture-recapture modelling provides a comprehensive and flexible framework for

  17. Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Panagiotis A Konstantinopoulos

    Full Text Available BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect". Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01, 1(st validation set (median OS 32 months versus not-yet-reached, p = 0.026 and 2(nd validation set (median OS 43 versus 61 months, p = 0.013 maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.

  18. Exploratory analysis of ERCC2 DNA methylation in survival among pediatric medulloblastoma patients.

    Science.gov (United States)

    Banfield, Emilyn; Brown, Austin L; Peckham, Erin C; Rednam, Surya P; Murray, Jeffrey; Okcu, M Fatih; Mitchell, Laura E; Chintagumpala, Murali M; Lau, Ching C; Scheurer, Michael E; Lupo, Philip J

    2016-10-01

    Medulloblastoma is the most frequent malignant pediatric brain tumor. While survival rates have improved due to multimodal treatment including cisplatin-based chemotherapy, there are few prognostic factors for adverse treatment outcomes. Notably, genes involved in the nucleotide excision repair pathway, including ERCC2, have been implicated in cisplatin sensitivity in other cancers. Therefore, this study evaluated the role of ERCC2 DNA methylation profiles on pediatric medulloblastoma survival. The study population included 71 medulloblastoma patients (age DNA methylation profiles were generated from peripheral blood samples using the Illumina Infinium Human Methylation 450 Beadchip. Sixteen ERCC2-associated CpG sites were evaluated in this analysis. Multivariable regression models were used to determine the adjusted association between DNA methylation and survival. Cox regression and Kaplan-Meier curves were used to compare 5-year overall survival between hyper- and hypo-methylation at each CpG site. In total, 12.7% (n=9) of the patient population died within five years of diagnosis. In our population, methylation of the cg02257300 probe (Hazard Ratio=9.33; 95% Confidence Interval: 1.17-74.64) was associated with death (log-rank p=0.01). This association remained suggestive after correcting for multiple comparisons (FDR pDNA methylation within the promoter region of the ERCC2 gene may be associated with survival in pediatric medulloblastoma. If confirmed in future studies, this information may lead to improved risk stratification or promote the development of novel, targeted therapeutics. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Modeling of thermal stresses and probability of survival of tubular SOFC

    Energy Technology Data Exchange (ETDEWEB)

    Nakajo, Arata [Laboratory for Industrial Energy Systems (LENI), Faculty of Engineering, Swiss Federal Institute of Technology, 1015 Lausanne (Switzerland); Stiller, Christoph; Bolland, Olav [Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim N-7491 (Norway); Haerkegaard, Gunnar [Department of Engineering Design and Materials, Norwegian University of Science and Technology, Trondheim N-7491 (Norway)

    2006-07-14

    The temperature profile generated by a thermo-electro-chemical model was used to calculate the thermal stress distribution in a tubular solid oxide fuel cell (SOFC). The solid heat balances were calculated separately for each layer of the MEA (membrane electrode assembly) in order to detect the radial thermal gradients more precisely. It appeared that the electrolyte undergoes high tensile stresses at the ends of the cell in limited areas and that the anode is submitted to moderate tensile stresses. A simplified version of the widely used Weibull analysis was used to calculate the global probability of survival for the assessment of the risks related to both operating points and load changes. The cell at room temperature was considered and revealed as critical. As a general trend, the computed probabilities of survival were too low for the typical requirements for a commercial product. A sensitivity analysis showed a strong influence of the thermal expansion mismatch between the layers of the MEA on the probability of survival. The lack of knowledge on mechanical material properties as well as uncertainties about the phenomena occurring in the cell revealed itself as a limiting parameter for the simulation of thermal stresses. (author)

  20. The ATLAS Analysis Model

    CERN Multimedia

    Amir Farbin

    The ATLAS Analysis Model is a continually developing vision of how to reconcile physics analysis requirements with the ATLAS offline software and computing model constraints. In the past year this vision has influenced the evolution of the ATLAS Event Data Model, the Athena software framework, and physics analysis tools. These developments, along with the October Analysis Model Workshop and the planning for CSC analyses have led to a rapid refinement of the ATLAS Analysis Model in the past few months. This article introduces some of the relevant issues and presents the current vision of the future ATLAS Analysis Model. Event Data Model The ATLAS Event Data Model (EDM) consists of several levels of details, each targeted for a specific set of tasks. For example the Event Summary Data (ESD) stores calorimeter cells and tracking system hits thereby permitting many calibration and alignment tasks, but will be only accessible at particular computing sites with potentially large latency. In contrast, the Analysis...

  1. Survival analysis under cross-sectional sampling : length bias and multiplicative censoring

    NARCIS (Netherlands)

    van Es, Bert; Klaassen, Chris A.J.; Oudshoorn, Karin

    2000-01-01

    Consider a parametric, nonparametric or semiparametric model for survival times. Interest is in estimation of Euclidean and Banach parameters for these models. However, not the survival times themselves will be observed, since this might be quite time consuming. Instead, cross-sectional sampling is

  2. Statistical Survival Analysis of Fish and Wildlife Tagging Studies; SURPH.1 Manual - Analysis of Release-Recapture Data for Survival Studies, 1994 Technical Manual.

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Steven G.; Skalski, John R.; Schelechte, J. Warren [Univ. of Washington, Seattle, WA (United States). Center for Quantitative Science

    1994-12-01

    Program SURPH is the culmination of several years of research to develop a comprehensive computer program to analyze survival studies of fish and wildlife populations. Development of this software was motivated by the advent of the PIT-tag (Passive Integrated Transponder) technology that permits the detection of salmonid smolt as they pass through hydroelectric facilities on the Snake and Columbia Rivers in the Pacific Northwest. Repeated detections of individually tagged smolt and analysis of their capture-histories permits estimates of downriver survival probabilities. Eventual installation of detection facilities at adult fish ladders will also permit estimation of ocean survival and upstream survival of returning salmon using the statistical methods incorporated in SURPH.1. However, the utility of SURPH.1 far exceeds solely the analysis of salmonid tagging studies. Release-recapture and radiotelemetry studies from a wide range of terrestrial and aquatic species have been analyzed using SURPH.1 to estimate discrete time survival probabilities and investigate survival relationships. The interactive computing environment of SURPH.1 was specifically developed to allow researchers to investigate the relationship between survival and capture processes and environmental, experimental and individual-based covariates. Program SURPH.1 represents a significant advancement in the ability of ecologists to investigate the interplay between morphologic, genetic, environmental and anthropogenic factors on the survival of wild species. It is hoped that this better understanding of risk factors affecting survival will lead to greater appreciation of the intricacies of nature and to improvements in the management of wild resources. This technical report is an introduction to SURPH.1 and provides a user guide for both the UNIX and MS-Windows{reg_sign} applications of the SURPH software.

  3. Modeling growth performances, survival, and feed efficiency of four ...

    African Journals Online (AJOL)

    Survival, feed efficiency and growth performances of four local breeds of chickens in West Cameroon (normally feathered NF, feathered tarsus FT, crested C and naked neck NN,) have been compared from hatch to 16 weeks, to determine which one could be improved by selection. Gompertz equation was used to fit growth ...

  4. Integrative genomic testing of cancer survival using semiparametric linear transformation models.

    Science.gov (United States)

    Huang, Yen-Tsung; Cai, Tianxi; Kim, Eunhee

    2016-07-20

    The wide availability of multi-dimensional genomic data has spurred increasing interests in integrating multi-platform genomic data. Integrative analysis of cancer genome landscape can potentially lead to deeper understanding of the biological process of cancer. We integrate epigenetics (DNA methylation and microRNA expression) and gene expression data in tumor genome to delineate the association between different aspects of the biological processes and brain tumor survival. To model the association, we employ a flexible semiparametric linear transformation model that incorporates both the main effects of these genomic measures as well as the possible interactions among them. We develop variance component tests to examine different coordinated effects by testing various subsets of model coefficients for the genomic markers. A Monte Carlo perturbation procedure is constructed to approximate the null distribution of the proposed test statistics. We further propose omnibus testing procedures to synthesize information from fitting various parsimonious sub-models to improve power. Simulation results suggest that our proposed testing procedures maintain proper size under the null and outperform standard score tests. We further illustrate the utility of our procedure in two genomic analyses for survival of glioblastoma multiforme patients. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. Surviving the present: Modeling tools for organizational change

    Energy Technology Data Exchange (ETDEWEB)

    Pangaro, P. (Pangaro Inc., Washington, DC (United States))

    1992-01-01

    The nuclear industry, like the rest of modern American business, is beset by a confluence of economic, technological, competitive, regulatory, and political pressures. For better or worse, business schools and management consultants have leapt to the rescue, offering the most modern conveniences that they can purvey. Recent advances in the study of organizations have led to new tools for their analysis, revision, and repair. There are two complementary tools that do not impose values or injunctions in themselves. One, called the organization modeler, captures the hierarchy of purposes that organizations and their subparts carry out. Any deficiency or pathology is quickly illuminated, and requirements for repair are made clear. The second, called THOUGHTSTICKER, is used to capture the semantic content of the conversations that occur across the interactions of parts of an organization. The distinctions and vocabulary in the language of an organization, and the relations within that domain, are elicited from the participants so that all three are available for debate and refinement. The product of the applications of these modeling tools is not the resulting models but rather the enhancement of the organization as a consequence of the process of constructing them.

  6. Increased flexibility for modeling telemetry and nest-survival data using the multistate framework

    Science.gov (United States)

    Devineau, Olivier; Kendall, William L.; Doherty, Paul F.; Shenk, Tanya M.; White, Gary C.; Lukacs, Paul M.; Burnham, Kenneth P.

    2014-01-01

    Although telemetry is one of the most common tools used in the study of wildlife, advances in the analysis of telemetry data have lagged compared to progress in the development of telemetry devices. We demonstrate how standard known-fate telemetry and related nest-survival data analysis models are special cases of the more general multistate framework. We present a short theoretical development, and 2 case examples regarding the American black duck and the mallard. We also present a more complex lynx data analysis. Although not necessary in all situations, the multistate framework provides additional flexibility to analyze telemetry data, which may help analysts and biologists better deal with the vagaries of real-world data collection.

  7. Development of a Model to Predict Transplant-free Survival of Patients With Acute Liver Failure.

    Science.gov (United States)

    Koch, David G; Tillman, Holly; Durkalski, Valerie; Lee, William M; Reuben, Adrian

    2016-08-01

    Patients with acute liver failure (ALF) have a high risk of death that can be substantially reduced with liver transplantation. It is a challenge to predict which patients with ALF will survive without liver transplant because available prognostic scoring systems are inadequate. We devised a mathematical model, using a large dataset collected by the Acute Liver Failure Study Group, which can predict transplant-free survival in patients with ALF. We performed a retrospective analysis of data from 1974 subjects who met criteria for ALF (coagulopathy and hepatic encephalopathy within 26 weeks of the first symptoms, without pre-existing liver disease) enrolled in the Acute Liver Failure Study Group database from January 1, 1998 through June 11, 2013. We randomly assigned the subjects to development and validation cohorts. Data from the development cohort were analyzed to identify factors associated with transplant-free survival (alive without transplantation by 21 days after admission to the study). Statistically significant variables were used to create a multivariable logistic regression model. Most subjects were women (70%) and white (78%); acetaminophen overdose was the most common cause (48% of subjects). The rate of transplant-free survival was 50%. Admission values of hepatic encephalopathy grade, ALF etiology, vasopressor use, and log transformations of bilirubin and international normalized ratio were significantly associated with transplant-free survival, based on logistic regression analysis. In the validation cohort, the resulting model predicted transplant-free survival with a C statistic value of 0.84, 66.3% accuracy (95% confidence interval, 63.1%-69.4%), 37.1% sensitivity (95% confidence interval, 32.5%-41.8%), and 95.3% specificity (95% confidence interval, 92.9%-97.1%). Using data from the Acute Liver Failure Study Group, we developed a model that predicts transplant-free survival of patients with ALF based on easily identifiable hospital admission

  8. Modeling survival: application of the Andersen-Gill model to Yellowstone grizzly bears

    Science.gov (United States)

    Johnson, Christopher J.; Boyce, Mark S.; Schwartz, Charles C.; Haroldson, Mark A.

    2004-01-01

     Wildlife ecologists often use the Kaplan-Meier procedure or Cox proportional hazards model to estimate survival rates, distributions, and magnitude of risk factors. The Andersen-Gill formulation (A-G) of the Cox proportional hazards model has seen limited application to mark-resight data but has a number of advantages, including the ability to accommodate left-censored data, time-varying covariates, multiple events, and discontinuous intervals of risks. We introduce the A-G model including structure of data, interpretation of results, and assessment of assumptions. We then apply the model to 22 years of radiotelemetry data for grizzly bears (Ursus arctos) of the Greater Yellowstone Grizzly Bear Recovery Zone in Montana, Idaho, and Wyoming, USA. We used Akaike's Information Criterion (AICc) and multi-model inference to assess a number of potentially useful predictive models relative to explanatory covariates for demography, human disturbance, and habitat. Using the most parsimonious models, we generated risk ratios, hypothetical survival curves, and a map of the spatial distribution of high-risk areas across the recovery zone. Our results were in agreement with past studies of mortality factors for Yellowstone grizzly bears. Holding other covariates constant, mortality was highest for bears that were subjected to repeated management actions and inhabited areas with high road densities outside Yellowstone National Park. Hazard models developed with covariates descriptive of foraging habitats were not the most parsimonious, but they suggested that high-elevation areas offered lower risks of mortality when compared to agricultural areas.

  9. Survival of hendra virus in the environment: modelling the effect of temperature.

    Science.gov (United States)

    Scanlan, J C; Kung, N Y; Selleck, P W; Field, H E

    2015-03-01

    Hendra virus (HeV), a highly pathogenic zoonotic paramyxovirus recently emerged from bats, is a major concern to the horse industry in Australia. Previous research has shown that higher temperatures led to lower virus survival rates in the laboratory. We develop a model of survival of HeV in the environment as influenced by temperature. We used 20 years of daily temperature at six locations spanning the geographic range of reported HeV incidents to simulate the temporal and spatial impacts of temperature on HeV survival. At any location, simulated virus survival was greater in winter than in summer, and in any month of the year, survival was higher in higher latitudes. At any location, year-to-year variation in virus survival 24 h post-excretion was substantial and was as large as the difference between locations. Survival was higher in microhabitats with lower than ambient temperature, and when environmental exposure was shorter. The within-year pattern of virus survival mirrored the cumulative within-year occurrence of reported HeV cases, although there were no overall differences in survival in HeV case years and non-case years. The model examines the effect of temperature in isolation; actual virus survivability will reflect the effect of additional environmental factors.

  10. Epidemiology and Survival Analysis of Jordanian Female Breast Cancer Patients Diagnosed from 1997 to 2002

    Directory of Open Access Journals (Sweden)

    Ghazi Sharkas

    2011-04-01

    Full Text Available Background: Breast cancer is the most common cancer among Jordanian women, yet survival data are scarce. This study aims to assess the observed five-year survival rate of breast cancer in Jordan from 1997 to 2002 and to determine factors that may influence survival. Methods: Data were obtained from the Jordan Cancer Registry (JCR, which is a population-based registry. From 1997-2002, 2121 patients diagnosed with breast cancer were registered in JCR. Relevant data were collected from JCR files, hospital medical records and histopathology reports. Patient's status, whether alive or dead, wasascertained from the Department of Civil Status using patients’ national numbers (ID. Statistical analysis was carried out using SPSS (version 10. Survival probabilities by age, morphology, grade, stage and other relevant variables were obtained with the Kaplan Meier method. Results: The overall five-year survival for breast cancer in Jordan, regardless of the stage or grade was 64.2%, meanwhile it was 58% in the group aged less than 30 years. The best survival was in the age group 40-49 years (69.3%. The survival for adenocarcinoma was 57.4% and for medullary carcinoma, it was 82%. The survival rate approximated 73.8% for well-differentiated, 55.6% for anaplastic, and 58% for poorly differentiated cancers. The five-year survival rate was 82.7% for stage I, 72.2% for stage II, 58.7% for stage III, and 34.6% for stage IV cancers.Conclusion: According to univariate analysis, stage, grade, age and laterality of breast cancer significantly influenced cancer survival. Cox regression analysis revealed that stage, grade and age factors correlated with prognosis, while laterality showed no significant effect on survival. Results demonstrated that overall survival was relatively poor. We hypothesized that this was due to low levels of awareness and lack of screening programs.

  11. Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

    Science.gov (United States)

    Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

    2017-09-01

    For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data.

    Science.gov (United States)

    McLain, Alexander C; Sundaram, Rajeshwari; Buck Louis, Germaine M

    2016-02-01

    The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. couples unable to achieve pregnancy. Modeling techniques conducted to date have largely relied upon discrete time-to-pregnancy survival or day-specific probability models to estimate the determinants of time-to-pregnancy or acute effects, respectively. We developed a class of semi-parametric grouped transformation cure models that capture day-level variates purported to affect the cycle-level hazards of conception and, possibly, sterility. Our model's performance is assessed using simulation and longitudinal data from one of the few prospective cohort studies with preconception enrollment of women followed for 12 menstrual cycles at risk for pregnancy. © The Author(s) 2012.

  13. Time-Dependent Tree-Structured Survival Analysis with Unbiased Variable Selection through Permutation Tests

    Science.gov (United States)

    Wallace, M. L.

    2014-01-01

    Incorporating time-dependent covariates into tree-structured survival analysis (TSSA) may result in more accurate prognostic models than if only baseline values are used. Available time-dependent TSSA methods exhaustively test every binary split on every covariate; however, this approach may result in selection bias towards covariates with more observed values. We present a method that uses unbiased significance levels from newly proposed permutation tests to select the time-dependent or baseline covariate with the strongest relationship with the survival outcome. The specific splitting value is identified using only the selected covariate. Simulation results show that the proposed time-dependent TSSA method produces tree models of equal or greater accuracy as compared to baseline TSSA models, even with high censoring rates and large within-subject variability in the time-dependent covariate. To illustrate, the proposed method is applied to data from a cohort of bipolar youth to identify subgroups at risk for self-injurious behavior. PMID:25043382

  14. Sex ratio estimation and survival analysis for Orthetrum coerulescens (Odonata, Libellulidae)

    Science.gov (United States)

    Kery, M.; Juillerat, L.

    2004-01-01

    There is controversy over whether uneven sex ratios observed in mature dragonfly populations are a mere artifact resulting from the higher observability of males. Previous studies have at best made indirect inference about sex ratios by analysis of survival or recapture rates. Here, we obtain direct estimates of sex ratio from capture?recapture data based on the Cormack?Jolly?Seber model. We studied Orthetrum coerulescens (Fabricius, 1798) at three sites in the Swiss Jura Mountains over an entire activity period. Recapture rates per 5-day interval were 3.5 times greater for males (0.67, SE 0.02) than for females (0.19, SE 0.02). At two sites, recapture rate increased over the season for males and was constant for females, and at one site it decreased with precipitation for both sexes. In addition, recapture rate was higher with higher temperature for males only. We found no evidence for higher male survival rates in any population. Survival per 5-day interval for both sexes was estimated to be 0.77 (95% CI 0.75?0.79) without significant site or time-specific variation. There were clear effects of temperature (positive) and precipitation (negative) on survival rate at two sites. Direct estimates of sex ratios were not significantly different from 1 for any time interval. Hence, the observed male-biased sex ratio in adult O. coerulescens was an artifact resulting from the better observability of males. The method presented in this paper is applicable to sex ratio estimation in any kind of animal.

  15. Using Survival Analysis to Describe Developmental Achievements of Early Intervention Recipients at Kindergarten

    Science.gov (United States)

    Scarborough, Anita A.; Hebbeler, Kathleen M.; Spiker, Donna; Simeonsson, Rune J.

    2011-01-01

    Survival analysis was used to document the developmental achievements of 2298 kindergarten children who participated in the National Early Intervention Longitudinal Study, a study that followed children from entry to Part C early intervention (EI) through kindergarten. Survival functions were produced depicting the percentage of children at…

  16. Effect of Body Mass Index on Overall Survival of Pancreatic Cancer: A Meta-Analysis.

    Science.gov (United States)

    Shi, Yu-Qi; Yang, Jing; Du, Peng; Xu, Ting; Zhuang, Xiao-Hui; Shen, Jia-Qing; Xu, Chun-Fang

    2016-04-01

    Although obesity has been identified as a risk factor for pancreatic cancer, the important question of whether obesity influences the prognosis of pancreatic cancer has not been explicated thoroughly. We therefore performed a meta-analysis to investigate the association between body mass index (BMI) and survival outcomes of patients with pancreatic cancer.Studies that described the relationship between BMI and overall survival (OS) of pancreatic cancer were searched in PubMed, Embase, Ovid, and Cochrane Library Databases from the earliest available date to May 12, 2015. Hazard ratios (HRs) for OS in each BMI category from individual studies were extracted and pooled by a random-effect model. Dose-response meta-analysis was also performed to estimate summary HR and 95% confidence interval (CI) for every 5-unit increment. Publication bias was evaluated by Begg funnel plot and Egger linear regression test.Ten relevant studies involving 6801 patients were finally included in the meta-analysis. Results showed that obesity in adulthood significantly shortened OS of pancreatic cancer patients (HR: 1.29, 95% CI: 1.17-1.41), whereas obesity at diagnosis was not associated with any increased risk of death (HR: 1.10, 95% CI: 0.78-1.42). For every 5-kg/m increment in adult BMI, the summary HR was 1.11 (95% CI: 1.05-1.18) for death risk of pancreatic cancer. However, no dose-response relationship was found in the BMI at diagnosis. Egger regression test and Begg funnel plot both revealed no obvious risk of publication bias.In conclusion, increased adult BMI is associated with increased risk of death for pancreatic cancer patients, which suggested that obesity in adulthood may be an important prognostic factor that indicates an abbreviated survival from pancreatic cancer. More studies are needed to validate this finding, and the mechanism behind the observation should be evaluated in further studies.

  17. Evaluation of red blood cell labelling methods based on a statistical model for red blood cell survival.

    Science.gov (United States)

    Korell, Julia; Coulter, Carolyn V; Duffull, Stephen B

    2011-12-21

    The aim of this work is to compare different labelling methods that are commonly used to estimate the lifespan of red blood cells (RBCs), e.g. in anaemia of renal failure, where the effect of treatment with erythropoietin depends on the lifespan of RBCs. A previously developed model for the survival time of RBCs that accounts for plausible physiological processes of RBC destruction was used to simulate ideal random and cohort labelling methods for RBCs, as well as the flaws associated with these methods (e.g. reuse of label and loss of the label from the surviving RBCs). Random labelling with radioactive chromium and cohort labelling using heavy nitrogen were considered. Blood sampling times were determined for RBC survival studies using both labelling methods by applying the theory of optimal design. It was assessed whether the underlying parameter values of the model are estimable from these studies, and the precision of the parameter estimates were calculated. In theory, parameter estimation would be possible for both types of ideal labelling methods without flaws. However, flaws associated with random labelling are significant and not all parameters controlling RBC survival in the model can be estimated with good precision. In contrast, cohort labelling shows good precision in the parameter estimates even in the presence of reuse and prolonged incorporation of the label. A model based analysis of RBC survival studies is recommended in future to account for limitations in methodology as well as likely causes of RBC destruction. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the international kidney cancer working group.

    Science.gov (United States)

    Manola, Judith; Royston, Patrick; Elson, Paul; McCormack, Jennifer Bacik; Mazumdar, Madhu; Négrier, Sylvie; Escudier, Bernard; Eisen, Tim; Dutcher, Janice; Atkins, Michael; Heng, Daniel Y C; Choueiri, Toni K; Motzer, Robert; Bukowski, Ronald

    2011-08-15

    To develop a single validated model for survival in metastatic renal cell carcinoma (mRCC) using a comprehensive international database. A comprehensive database of 3,748 patients including previously reported clinical prognostic factors was established by pooling patient-level data from clinical trials. Following quality control and standardization, descriptive statistics were generated. Univariate analyses were conducted using proportional hazards models. Multivariable analysis using a log-logistic model stratified by center and multivariable fractional polynomials was conducted to identify independent predictors of survival. Missing data were handled using multiple imputation methods. Three risk groups were formed using the 25th and 75th percentiles of the resulting prognostic index. The model was validated using an independent data set of 645 patients treated with tyrosine kinase inhibitor (TKI) therapy. Median survival in the favorable, intermediate and poor risk groups was 26.9 months, 11.5 months, and 4.2 months, respectively. Factors contributing to the prognostic index included treatment, performance status, number of metastatic sites, time from diagnosis to treatment, and pretreatment hemoglobin, white blood count, lactate dehydrogenase, alkaline phosphatase, and serum calcium. The model showed good concordance when tested among patients treated with TKI therapy (C statistic = 0.741, 95% CI: 0.714-0.768). Nine clinical factors can be used to model survival in mRCC and form distinct prognostic groups. The model shows utility among patients treated in the TKI era. ©2011 AACR.

  19. Long-Term Survival Outcomes of Cancer-Directed Surgery for Malignant Pleural Mesothelioma: Propensity Score Matching Analysis.

    Science.gov (United States)

    Nelson, David B; Rice, David C; Niu, Jiangong; Atay, Scott; Vaporciyan, Ara A; Antonoff, Mara; Hofstetter, Wayne L; Walsh, Garrett L; Swisher, Stephen G; Roth, Jack A; Tsao, Anne; Gomez, Daniel; Giordano, Sharon H; Mehran, Reza; Sepesi, Boris

    2017-10-10

    Purpose Small observational studies have shown a survival advantage to undergoing cancer-directed surgery for malignant pleural mesothelioma (MPM); however, it is unclear if these results are generalizable. Our purpose was to evaluate survival after treatment of MPM with cancer-directed surgery and to explore the effect surgery interaction with chemotherapy or radiation therapy on survival by using the National Cancer Database. Patients and Methods Patients with microscopically proven MPM were identified within the National Cancer Database (2004 to 2014). Propensity score matching was performed 1:2 and among this cohort, a Cox proportional hazards regression model was used to identify predictors of survival. Median survival was calculated by using the Kaplan-Meier method. Results Of 20,561 patients with MPM, 6,645 were identified in the matched cohort, among whom 2,166 underwent no therapy, 2,015 underwent chemotherapy alone, 850 underwent cancer-directed surgery alone, 988 underwent surgery with chemotherapy, and 274 underwent trimodality therapy. The remaining 352 patients underwent another combination of surgery, radiation, or chemotherapy. Thirty-day and 90-day mortality rates were 6.3% and 15.5%. Cancer-directed surgery, chemotherapy, and radiation therapy were independently associated with improved survival (hazard ratio, 0.77, 0.74, and 0.88, respectively). Stratified analysis revealed that surgery-based multimodality therapy demonstrated an improved survival compared with surgery alone, with no significant difference between surgery-based multimodality therapies; however, the largest estimated effect was when cancer-directed surgery, chemotherapy, and radiation therapy were combined (hazard ratio, 0.52). For patients with the epithelial subtype who underwent trimodality therapy, median survival was extended from 14.5 months to 23.4 months. Conclusion MPM is an aggressive and rapidly fatal disease. Surgery-based multimodality therapy was associated with

  20. Prognostic Model for Survival in Patients With Early Stage Cervical Cancer

    NARCIS (Netherlands)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J.; Stalpers, Lukas J. A.; Schilthuis, Marten S.; van der Steeg, Jan Willem; Burger, Matthé P. M.; Buist, Marrije R.

    2011-01-01

    BACKGROUND: In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer

  1. Role of Metastasis in Hypertabastic Survival Analysis of Breast Cancer: Interaction with Clinical and Gene Expression Variables

    Directory of Open Access Journals (Sweden)

    Mohammad A. Tabatabai Ph.D.

    2012-01-01

    Full Text Available This paper analyzes the survival of breast cancer patients, exploring the role of a metastasis variable in combination with clinical and gene expression variables. We use the hypertabastic model in a detailed analysis of 295 breast cancer patients from the Netherlands Cancer Institute given in. 1 In comparison to Cox regression the increase in accuracy is complemented by the ability to analyze the time course of the disease progression using the explicitly described hazard and survival curves. We also demonstrate the ability to compute deciles for survival and probability of survival to a given time. Our primary concern in this article is the introduction of a variable representing the existence of metastasis and the effects on the other clinical and gene expression variables. In addition to making a quantitative assessment of the impact of metastasis on the prospects for survival, we are able to look at its interactions with the other prognostic variables. The estrogen receptor status increase in importance, while the significance of the gene expression variables used in the combined model diminishes. When considering only the subgroup of patients who experienced metastasis, the covariates in the model are only the clinical variables for estrogen receptor status and tumor grade.

  2. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.

    2016-02-09

    We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information.

  3. Foster Care Reentry: A survival analysis assessing differences across permanency type.

    Science.gov (United States)

    Goering, Emily Smith; Shaw, Terry V

    2017-06-01

    Foster care reentry is an important factor for evaluating the overall success of permanency. Rates of reentry are typically only measured for 12-months and are often evaluated only for children who exit foster care to reunification and not across exit types, also known as 'permanency types'. This study examined the odds of reentry across multiple common permanency types for a cohort of 8107 children who achieved permanency between 2009 and 2013. Overall, 14% of children reentered care within 18-months with an average time to reentry of 6.36 months. A Kaplan-Meier survival analysis was used to assess differences in reentry across permanency types (including reunification, relative guardianship and non-relative guardianship). Children who achieved guardianship with kin had the lowest odds of reentry overall, followed by guardianship with non-kin, and reunification with family of origin. Children reunifying against the recommendations of Children and Family Services had the highest odds of reentry. A Cox regression survival analysis was conducted to assess odds of reentry across permanency type while controlling for demographics, services, and other risk factors. In the final model, only permanency type and cumulative risk were found to have a statistically significant impact on odds of reentry. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Predicting secondary school dropout among South African adolescents: A survival analysis approach

    National Research Council Canada - National Science Library

    Xie, Hui (Jimmy); Caldwell, Linda L; Smith, Edward A; Weybright, Elizabeth H; Wegner, Lisa

    2017-01-01

    ...% of the age appropriate population remain enrolled. Survival analysis was used to identify the risk of dropping out of secondary school for male and female adolescents and examine the influence of substance use and leisure experience predictors...

  5. PROGNOSTIC FACTORS AND SURVIVAL ANALYSIS IN ESOPHAGEAL CARCINOMA.

    Science.gov (United States)

    Tustumi, Francisco; Kimura, Cintia Mayumi Sakurai; Takeda, Flavio Roberto; Uema, Rodrigo Hideki; Salum, Rubens Antônio Aissar; Ribeiro-Junior, Ulysses; Cecconello, Ivan

    2016-01-01

    Despite recent advances in diagnosis and treatment, esophageal cancer still has high mortality. Prognostic factors associated with patient and with disease itself are multiple and poorly explored. Assess prognostic variables in esophageal cancer patients. Retrospective review of all patients with esophageal cancer in an oncology referral center. They were divided according to histological diagnosis (444 squamous cell carcinoma patients and 105 adenocarcinoma), and their demographic, pathological and clinical characteristics were analyzed and compared to clinical stage and overall survival. No difference was noted between squamous cell carcinoma and esophageal adenocarcinoma overall survival curves. Squamous cell carcinoma presented 22.8% survival after five years against 20.2% for adenocarcinoma. When considering only patients treated with curative intent resection, after five years squamous cell carcinoma survival rate was 56.6 and adenocarcinoma, 58%. In patients with squamous cell carcinoma, poor differentiation histology and tumor size were associated with worse oncology stage, but this was not evidenced in adenocarcinoma. Weight loss (kg), BMI variation (kg/m²) and percentage of weight loss are factors that predict worse stage at diagnosis in the squamous cell carcinoma. In adenocarcinoma, these findings were not statistically significant. Apesar dos avanços recentes nos métodos diagnósticos e tratamento, o câncer de esôfago mantém alta mortalidade. Fatores prognósticos associados ao paciente e ao câncer propriamente dito são pouco conhecidos. Investigar variáveis prognósticas no câncer esofágico. Pacientes diagnosticados entre 2009 e 2012 foram analisados e subdivididos de acordo com tipo histológico (444 carcinomas espinocelulares e 105 adenocarcinomas), e então características demográficas, anatomopatológicas e clínicas foram analisadas. Não houve diferença entre os dois tipos histológicos na sobrevida global. Carcinoma espinocelular

  6. Acute Myeloid Leukemia: analysis of epidemiological profile and survival rate.

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    de Lima, Mariana Cardoso; da Silva, Denise Bousfield; Freund, Ana Paula Ferreira; Dacoregio, Juliana Shmitz; Costa, Tatiana El Jaick Bonifácio; Costa, Imaruí; Faraco, Daniel; Silva, Maurício Laerte

    2016-01-01

    To describe the epidemiological profile and the survival rate of patients with acute myeloid leukemia (AML) in a state reference pediatric hospital. Clinical-epidemiological, observational, retrospective, descriptive study. The study included new cases of patients with AML, diagnosed between 2004 and 2012, younger than 15 years. Of the 51 patients studied, 84% were white; 45% were females and 55%, males. Regarding age, 8% were younger than 1 year, 47% were aged between 1 and 10 years, and 45% were older than 10 years. The main signs/symptoms were fever (41.1%), asthenia/lack of appetite (35.2%), and hemorrhagic manifestations (27.4%). The most affected extra-medullary site was the central nervous system (14%). In 47% of patients, the white blood cell (WBC) count was below 10,000/mm(3) at diagnosis. The minimal residual disease (MRD) was less than 0.1%, on the 15th day of treatment in 16% of the sample. Medullary relapse occurred in 14% of cases. When comparing the bone marrow MRD with the vital status, it was observed that 71.42% of the patients with type M3 AML were alive, as were 54.05% of those with non-M3 AML. The death rate was 43% and the main proximate cause was septic shock (63.6%). In this study, the majority of patients were male, white, and older than 1 year. Most patients with WBC count <10,000/mm(3) at diagnosis lived. Overall survival was higher in patients with MRD <0.1%. The prognosis was better in patients with AML-M3. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  7. Environmental enrichment extends photoreceptor survival and visual function in a mouse model of retinitis pigmentosa.

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    Ilaria Barone

    Full Text Available Slow, progressive rod degeneration followed by cone death leading to blindness is the pathological signature of all forms of human retinitis pigmentosa (RP. Therapeutic schemes based on intraocular delivery of neuroprotective agents prolong the lifetime of photoreceptors and have reached the stage of clinical trial. The success of these approaches depends upon optimization of chronic supply and appropriate combination of factors. Environmental enrichment (EE, a novel neuroprotective strategy based on enhanced motor, sensory and social stimulation, has already been shown to exert beneficial effects in animal models of various disorders of the CNS, including Alzheimer and Huntington disease. Here we report the results of prolonged exposure of rd10 mice, a mutant strain undergoing progressive photoreceptor degeneration mimicking human RP, to such an enriched environment from birth. By means of microscopy of retinal tissue, electrophysiological recordings, visual behaviour assessment and molecular analysis, we show that EE considerably preserves retinal morphology and physiology as well as visual perception over time in rd10 mutant mice. We find that protective effects of EE are accompanied by increased expression of retinal mRNAs for CNTF and mTOR, both factors known as instrumental to photoreceptor survival. Compared to other rescue approaches used in similar animal models, EE is highly effective, minimally invasive and results into a long-lasting retinal protection. These results open novel perspectives of research pointing to environmental strategies as useful tools to extend photoreceptor survival.

  8. Survival prediction based on compound covariate under Cox proportional hazard models.

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    Takeshi Emura

    Full Text Available Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package "compound.Cox" available in CRAN at http://cran.r-project.org/.

  9. Associations between statin use and non-Hodgkin lymphoma (NHL) risk and survival: a meta-analysis.

    Science.gov (United States)

    Ye, Xibiao; Mneina, Ayat; Johnston, James B; Mahmud, Salaheddin M

    2017-06-01

    Evidence on the effect of statin use on non-Hodgkin lymphoma (NHL) is not clear. We conducted a systematic review and meta-analysis to examine the associations between statin use and NHL risk and survival. We searched multiple literature sources up to October 2014 and identified 10 studies on the risk of diagnosis with NHL and 9 studies on survival. Random effects model was used to calculate pooled odds ratio (PORs) for risk and pooled hazard ratio (PHR) for survival. Heterogeneity among studies was examined using the Tau-squared and the I-squared (I 2 ) tests. Statin use was associated with reduced risk for total NHL (POR = 0.82, 95% CI 0.69-0.99). Among statin users, there was a lower incidence risk for marginal zone lymphoma (POR = 0.54, 95% CI 0.31-0.94), but this was not observed for other types of NHL. However, statin use did not affect overall survival (PHR = 1.02, 95% CI 0.99-1.06) or event-free survival (PHR = 0.99, 95% CI 0.87-1.12) in diffuse large B-cell lymphoma. There is suggestive epidemiological evidence that statins decrease the risk of NHL, but they do not influence survival in NHL patients. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  10. Fasudil improves survival and promotes skeletal muscle development in a mouse model of spinal muscular atrophy

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    Bowerman Melissa

    2012-03-01

    Full Text Available Abstract Background Spinal muscular atrophy (SMA is the leading genetic cause of infant death. It is caused by mutations/deletions of the survival motor neuron 1 (SMN1 gene and is typified by the loss of spinal cord motor neurons, muscular atrophy, and in severe cases, death. The SMN protein is ubiquitously expressed and various cellular- and tissue-specific functions have been investigated to explain the specific motor neuron loss in SMA. We have previously shown that the RhoA/Rho kinase (ROCK pathway is misregulated in cellular and animal SMA models, and that inhibition of ROCK with the chemical Y-27632 significantly increased the lifespan of a mouse model of SMA. In the present study, we evaluated the therapeutic potential of the clinically approved ROCK inhibitor fasudil. Methods Fasudil was administered by oral gavage from post-natal day 3 to 21 at a concentration of 30 mg/kg twice daily. The effects of fasudil on lifespan and SMA pathological hallmarks of the SMA mice were assessed and compared to vehicle-treated mice. For the Kaplan-Meier survival analysis, the log-rank test was used and survival curves were considered significantly different at P t test for paired variables and one-way analysis of variance (ANOVA were used to test for differences between samples and data were considered significantly different at P Results Fasudil significantly improves survival of SMA mice. This dramatic phenotypic improvement is not mediated by an up-regulation of Smn protein or via preservation of motor neurons. However, fasudil administration results in a significant increase in muscle fiber and postsynaptic endplate size, and restores normal expression of markers of skeletal muscle development, suggesting that the beneficial effects of fasudil could be muscle-specific. Conclusions Our work underscores the importance of muscle as a therapeutic target in SMA and highlights the beneficial potential of ROCK inhibitors as a therapeutic strategy for SMA

  11. A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs.

    Science.gov (United States)

    Li, Xianqiu; An, Zhaoling; Li, Peihui; Liu, Haihua

    2017-09-01

    There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.

  12. Modeling age and nest-specific survival using a hierarchical Bayesian approach.

    Science.gov (United States)

    Cao, Jing; He, Chong Z; Suedkamp Wells, Kimberly M; Millspaugh, Joshua J; Ryan, Mark R

    2009-12-01

    Recent studies have shown that grassland birds are declining more rapidly than any other group of terrestrial birds. Current methods of estimating avian age-specific nest survival rates require knowing the ages of nests, assuming homogeneous nests in terms of nest survival rates, or treating the hazard function as a piecewise step function. In this article, we propose a Bayesian hierarchical model with nest-specific covariates to estimate age-specific daily survival probabilities without the above requirements. The model provides a smooth estimate of the nest survival curve and identifies the factors that are related to the nest survival. The model can handle irregular visiting schedules and it has the least restrictive assumptions compared to existing methods. Without assuming proportional hazards, we use a multinomial semiparametric logit model to specify a direct relation between age-specific nest failure probability and nest-specific covariates. An intrinsic autoregressive prior is employed for the nest age effect. This nonparametric prior provides a more flexible alternative to the parametric assumptions. The Bayesian computation is efficient because the full conditional posterior distributions either have closed forms or are log concave. We use the method to analyze a Missouri dickcissel dataset and find that (1) nest survival is not homogeneous during the nesting period, and it reaches its lowest at the transition from incubation to nestling; and (2) nest survival is related to grass cover and vegetation height in the study area.

  13. Model of white oak flower survival and maturation

    Science.gov (United States)

    David R. Larsen; Robert A. Cecich

    1997-01-01

    A stochastic model of oak flower dynamics is presented that integrates a number of factors which appear to affect the oak pistillate flower development process. The factors are modeled such that the distribution of the predicted flower populations could have come from the same distribution as the observed flower populations. Factors included in the model are; the range...

  14. Comparing measurement error correction methods for rate-of-change exposure variables in survival analysis.

    Science.gov (United States)

    Veronesi, Giovanni; Ferrario, Marco M; Chambless, Lloyd E

    2013-12-01

    In this article we focus on comparing measurement error correction methods for rate-of-change exposure variables in survival analysis, when longitudinal data are observed prior to the follow-up time. Motivational examples include the analysis of the association between changes in cardiovascular risk factors and subsequent onset of coronary events. We derive a measurement error model for the rate of change, estimated through subject-specific linear regression, assuming an additive measurement error model for the time-specific measurements. The rate of change is then included as a time-invariant variable in a Cox proportional hazards model, adjusting for the first time-specific measurement (baseline) and an error-free covariate. In a simulation study, we compared bias, standard deviation and mean squared error (MSE) for the regression calibration (RC) and the simulation-extrapolation (SIMEX) estimators. Our findings indicate that when the amount of measurement error is substantial, RC should be the preferred method, since it has smaller MSE for estimating the coefficients of the rate of change and of the variable measured without error. However, when the amount of measurement error is small, the choice of the method should take into account the event rate in the population and the effect size to be estimated. An application to an observational study, as well as examples of published studies where our model could have been applied, are also provided.

  15. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

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    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  16. The log-Burr XII regression model for grouped survival data.

    Science.gov (United States)

    Hashimoto, Elizabeth M; Ortega, Edwin M M; Cordeiro, Gauss M; Barreto, Mauricio L

    2012-01-01

    The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

  17. Permanent teeth pulpotomy survival analysis: retrospective follow-up.

    Science.gov (United States)

    Kunert, Gustavo Golgo; Kunert, Itaborai Revoredo; da Costa Filho, Luiz Cesar; de Figueiredo, José Antônio Poli

    2015-09-01

    The aim of the present study is to evaluate risk factors influencing the success rates of pulpotomies both in young and adult populations. Pulpotomies (n=273) performed by a single endodontic specialist were analyzed, and data on success rates were collected. Additionally, possible explanatory variables were noted such as: age, gender, clinical findings (teeth, type of restoration after pulpotomy), radiographic findings (dentin bridge formation) and systemic conditions. The follow-up period varied from 1 to 29 years, and the results were analyzed by Kaplan-Meier survival curves and also by Cox regression. Age at the time of pulpotomy ranged from 8 to 79 and had not influenced the success rates (p=0.35). The formation of dentin bridge had a strong protective effect (hazard ratio-HR=0.16, ppulpotomy had the smallest failure rate, and amalgam has not increased the risk of failure significantly in relation to prosthesis. Resin composite restorations following pulpotomy increased in 263% the risk of failure (HR=3.63, ppulpotomy may be a successful treatment at any age, and not only for young permanent teeth. It was also possible to conclude that the use of direct composite restorations following pulpotomies is associated with higher failure rates. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Cabozantinib versus everolimus, nivolumab, axitinib, sorafenib and best supportive care: A network meta-analysis of progression-free survival and overall survival in second line treatment of advanced renal cell carcinoma.

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    Billy Amzal

    Full Text Available Relative effect of therapies indicated for the treatment of advanced renal cell carcinoma (aRCC after failure of first line treatment is currently not known. The objective of the present study is to evaluate progression-free survival (PFS and overall survival (OS of cabozantinib compared to everolimus, nivolumab, axitinib, sorafenib, and best supportive care (BSC in aRCC patients who progressed after previous VEGFR tyrosine-kinase inhibitor (TKI treatment.Systematic literature search identified 5 studies for inclusion in this analysis. The assessment of the proportional hazard (PH assumption between the survival curves for different treatment arms in the identified studies showed that survival curves in two of the studies did not fulfil the PH assumption, making comparisons of constant hazard ratios (HRs inappropriate. Consequently, a parametric survival network meta-analysis model was implemented with five families of functions being jointly fitted in a Bayesian framework to PFS, then OS, data on all treatments. The comparison relied on data digitized from the Kaplan-Meier curves of published studies, except for cabozantinib and its comparator everolimus where patient level data were available. This analysis applied a Bayesian fixed-effects network meta-analysis model to compare PFS and OS of cabozantinib versus its comparators. The log-normal fixed-effects model displayed the best fit of data for both PFS and OS, and showed that patients on cabozantinib had a higher probability of longer PFS and OS than patients exposed to comparators. The survival advantage of cabozantinib increased over time for OS. For PFS the survival advantage reached its maximum at the end of the first year's treatment and then decreased over time to zero.With all five families of distributions, cabozantinib was superior to all its comparators with a higher probability of longer PFS and OS during the analyzed 3 years, except with the Gompertz model, where nivolumab was

  19. Integrated survival analysis using an event-time approach in a Bayesian framework

    Science.gov (United States)

    Walsh, Daniel P.; Dreitz, VJ; Heisey, Dennis M.

    2015-01-01

    Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

  20. Cisplatin plus paclitaxel and maintenance of bevacizumab on tumour progression, dissemination, and survival of ovarian carcinoma xenograft models.

    Science.gov (United States)

    Oliva, P; Decio, A; Castiglioni, V; Bassi, A; Pesenti, E; Cesca, M; Scanziani, E; Belotti, D; Giavazzi, R

    2012-07-10

    Bevacizumab is being incorporated as first-line therapy with standard-of-care chemotherapy on epithelial ovarian carcinoma (EOC). We investigated bevacizumab combined with chemotherapy on tumour progression and mouse survival in EOC xenograft models. Bevacizumab was administered concomitantly with cisplatin plus paclitaxel (DDP+PTX), continued after induction (maintenance) or started after chemotherapy. The effect on tumour progression was monitored by bioluminescence imaging (BLI) (1A9-luc xenograft). Tumour dissemination into the peritoneal organs and ascites formation (HOC22 xenograft) was evaluated by histological analysis at the end of treatment (interim) and at euthanasia (survival). The effects on overall survival (OS) were investigated in both EOC models. Bevacizumab with PTX+DDP delayed tumour progression in mice bearing EOC xenografts. OS was significantly extended, with complete responses, by bevacizumab continued after stopping chemotherapy in the HOC22 xenograft. Bevacizumab alone inhibited ascites formation, with only limited effect on tumour burden, but combined with PTX+DDP reduced ascites and metastases. Bevacizumab started after induction with PTX+DDP and maintained was equally effective on tumour progression and survival on 1A9-luc xenograft. Bevacizumab combined with chemotherapy not only affected tumour progression, but when administered as maintenance regimen significantly prolonged survival, reducing ascites, and tumour dissemination. We believe our findings are consistent with the clinical results and shed light on the potential effects of this kind of treatment on tumour progression.

  1. Acute lymphoblastic leukemia in children and adolescents: prognostic factors and analysis of survival

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    Daniel Willian Lustosa de Sousa

    2015-08-01

    Full Text Available OBJECTIVE: To describe the clinical and laboratory features of children and adolescents with acute lymphoblastic leukemia treated at three referral centers in Ceará and evaluate prognostic factors for survival, including age, gender, presenting white blood cell count, immunophenotype, DNA index and early response to treatment.METHODS: Seventy-six under 19-year-old patients with newly diagnosed acute lymphoblastic leukemia treated with the Grupo Brasileiro de Tratamento de Leucemia da Infância - acute lymphoblastic leukemia-93 and -99 protocols between September 2007 and December 2009 were analyzed. The diagnosis was based on cytological, immunophenotypic and cytogenetic criteria. Associations between variables, prognostic factors and response to treatment were analyzed using the chi-square test and Fisher's exact test. Overall and event-free survival were estimated by Kaplan-Meier analysis and compared using the log-rank test. A Cox proportional hazards model was used to identify independent prognostic factors.RESULTS: The average age at diagnosis was 6.3 ± 0.5 years and males were predominant (65%. The most frequently observed clinical features were hepatomegaly, splenomegaly and lymphadenopathy. Central nervous system involvement and mediastinal enlargement occurred in 6.6% and 11.8%, respectively. B-acute lymphoblastic leukemia was more common (89.5% than T-acute lymphoblastic leukemia. A DNA index >1.16 was found in 19% of patients and was associated with favorable prognosis. On Day 8 of induction therapy, 95% of the patients had lymphoblast counts <1000/µL and white blood cell counts <5.0 Ã- 109/L. The remission induction rate was 95%, the induction mortality rate was 2.6% and overall survival was 72%.CONCLUSION: The prognostic factors identified are compatible with the literature. The 5-year overall and event-free survival rates were lower than those reported for developed countries. As shown by the multivariate analysis, age

  2. Acute lymphoblastic leukemia in children and adolescents: prognostic factors and analysis of survival

    Science.gov (United States)

    Lustosa de Sousa, Daniel Willian; de Almeida Ferreira, Francisco Valdeci; Cavalcante Félix, Francisco Helder; de Oliveira Lopes, Marcos Vinicios

    2015-01-01

    Objective To describe the clinical and laboratory features of children and adolescents with acute lymphoblastic leukemia treated at three referral centers in Ceará and evaluate prognostic factors for survival, including age, gender, presenting white blood cell count, immunophenotype, DNA index and early response to treatment. Methods Seventy-six under 19-year-old patients with newly diagnosed acute lymphoblastic leukemia treated with the Grupo Brasileiro de Tratamento de Leucemia da Infância – acute lymphoblastic leukemia-93 and -99 protocols between September 2007 and December 2009 were analyzed. The diagnosis was based on cytological, immunophenotypic and cytogenetic criteria. Associations between variables, prognostic factors and response to treatment were analyzed using the chi-square test and Fisher's exact test. Overall and event-free survival were estimated by Kaplan–Meier analysis and compared using the log-rank test. A Cox proportional hazards model was used to identify independent prognostic factors. Results The average age at diagnosis was 6.3 ± 0.5 years and males were predominant (65%). The most frequently observed clinical features were hepatomegaly, splenomegaly and lymphadenopathy. Central nervous system involvement and mediastinal enlargement occurred in 6.6% and 11.8%, respectively. B-acute lymphoblastic leukemia was more common (89.5%) than T-acute lymphoblastic leukemia. A DNA index >1.16 was found in 19% of patients and was associated with favorable prognosis. On Day 8 of induction therapy, 95% of the patients had lymphoblast counts <1000/μL and white blood cell counts <5.0 × 109/L. The remission induction rate was 95%, the induction mortality rate was 2.6% and overall survival was 72%. Conclusion The prognostic factors identified are compatible with the literature. The 5-year overall and event-free survival rates were lower than those reported for developed countries. As shown by the multivariate analysis, age and baseline white

  3. Statin use and kidney cancer survival outcomes: A systematic review and meta-analysis.

    Science.gov (United States)

    Nayan, Madhur; Punjani, Nahid; Juurlink, David N; Finelli, Antonio; Austin, Peter C; Kulkarni, Girish S; Uleryk, Elizabeth; Hamilton, Robert J

    2017-01-01

    Statin use has been associated with improved survival outcomes in various malignancies. Randomized controlled trials are currently underway evaluating their utility as adjunctive cancer therapies. However, studies evaluating the association between statin use and outcomes in kidney cancer yield conflicting results. We searched MEDLINE and EMBASE to identify studies evaluating the association between statin use and kidney cancer survival outcomes. We evaluated risk of bias with the Newcastle-Ottawa Scale. We pooled hazard ratios for recurrence-free survival, progression-free survival, cancer-specific survival, and overall survival using random-effects models. We evaluated publication bias through Begg's and Egger's tests, and the trim and fill procedure. We identified 12 studies meeting inclusion criteria and summarized data from 18,105 patients. No study was considered to be at high risk of bias. Statin use was not significantly associated with recurrence-free survival (pooled HR 0.97, 95% CI 0.89-1.06) or progression-free survival (pooled HR 0.92, 95% CI 0.51-1.65); however, statin use was associated with marked improvements in cancer-specific survival (pooled HR 0.67, 95% CI 0.47-0.94) and overall survival (pooled HR 0.74, 95% CI 0.63-0.88). There was no strong evidence of publication bias for any outcome. Our results demonstrate that statin use among patients with kidney cancer is associated with significantly improved cancer-specific and overall survival. Further studies are needed to confirm the therapeutic role of statins in kidney cancer. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

    Science.gov (United States)

    Nasejje, Justine B; Mwambi, Henry; Dheda, Keertan; Lesosky, Maia

    2017-07-28

    Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

  5. Connecting single-stock assessment models through correlated survival

    DEFF Research Database (Denmark)

    Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro

    2017-01-01

    Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long ru...

  6. Generation of a convalescent model of virulent Francisella tularensis infection for assessment of host requirements for survival of tularemia.

    Directory of Open Access Journals (Sweden)

    Deborah D Crane

    Full Text Available Francisella tularensis is a facultative intracellular bacterium and the causative agent of tularemia. Development of novel vaccines and therapeutics for tularemia has been hampered by the lack of understanding of which immune components are required to survive infection. Defining these requirements for protection against virulent F. tularensis, such as strain SchuS4, has been difficult since experimentally infected animals typically die within 5 days after exposure to as few as 10 bacteria. Such a short mean time to death typically precludes development, and therefore assessment, of immune responses directed against virulent F. tularensis. To enable identification of the components of the immune system that are required for survival of virulent F. tularensis, we developed a convalescent model of tularemia in C57Bl/6 mice using low dose antibiotic therapy in which the host immune response is ultimately responsible for clearance of the bacterium. Using this model we demonstrate αβTCR(+ cells, γδTCR(+ cells, and B cells are necessary to survive primary SchuS4 infection. Analysis of mice deficient in specific soluble mediators shows that IL-12p40 and IL-12p35 are essential for survival of SchuS4 infection. We also show that IFN-γ is required for survival of SchuS4 infection since mice lacking IFN-γR succumb to disease during the course of antibiotic therapy. Finally, we found that both CD4(+ and CD8(+ cells are the primary producers of IFN-γand that γδTCR(+ cells and NK cells make a minimal contribution toward production of this cytokine throughout infection. Together these data provide a novel model that identifies key cells and cytokines required for survival or exacerbation of infection with virulent F. tularensis and provides evidence that this model will be a useful tool for better understanding the dynamics of tularemia infection.

  7. Modeling of pathogen survival during simulated gastric digestion.

    Science.gov (United States)

    Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

    2011-02-01

    The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

  8. Modeling of Pathogen Survival during Simulated Gastric Digestion ▿

    Science.gov (United States)

    Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

    2011-01-01

    The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens. PMID:21131530

  9. Modelling Tradescantia fluminensis to assess long term survival

    Directory of Open Access Journals (Sweden)

    Alex James

    2015-06-01

    Full Text Available We present a simple Poisson process model for the growth of Tradescantia fluminensis, an invasive plant species that inhibits the regeneration of native forest remnants in New Zealand. The model was parameterised with data derived from field experiments in New Zealand and then verified with independent data. The model gave good predictions which showed that its underlying assumptions are sound. However, this simple model had less predictive power for outputs based on variance suggesting that some assumptions were lacking. Therefore, we extended the model to include higher variability between plants thereby improving its predictions. This high variance model suggests that control measures that promote node death at the base of the plant or restrict the main stem growth rate will be more effective than those that reduce the number of branching events. The extended model forms a good basis for assessing the efficacy of various forms of control of this weed, including the recently-released leaf-feeding tradescantia leaf beetle (Neolema ogloblini.

  10. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Liu, Kevin X; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J; Davies, Ben; Davies, Kay E; Oliver, Peter L

    2015-05-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1(G93A) mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1(G93A) mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

  11. Risk factors for dental caries in childhood: a five-year survival analysis.

    Science.gov (United States)

    Lee, Hyo-Jin; Kim, Jin-Bom; Jin, Bo-Hyoung; Paik, Dai-Il; Bae, Kwang-Hak

    2015-04-01

    The purpose of this study was to examine the risk factors of dental caries at the level of an individual person with survival analysis of the prospective data for 5 years. A total of 249 first-grade students participated in a follow-up study for 5 years. All participants responded to a questionnaire inquiring about socio-demographic variables and oral health behaviors. They also received an oral examination and were tested for Dentocult SM and LB. Over 5 years, the participants received yearly oral follow-up examinations to determine the incidence of dental caries. The incidence of one or more dental caries (DC1) and four or more dental caries (DC4) were defined as one or more and four or more decayed, missing, and filled permanent teeth increments, respectively. Socio-demographic variables, oral health behaviors, and status and caries activity tests were assessed as risk factors for DC1 and DC4. The adjusted hazard ratios (HRs) of risk factors for DC1 and DC4 were calculated using Cox proportional hazard regression models. During the 5-year follow-up period, DC1 and DC4 occurred in 87 and 25 participants, respectively. In multivariate hazard models, five or more decayed, missing, and filled primary molar teeth [HR 1.93, 95% confidence interval (CI) 1.19-3.13], and Dentocult LB of two or three (HR 2.21, 95% CI 1.37-3.56) were independent risk factors of DC1. For DC4, only Dentocult LB of two or three was an independent risk factor (HR 2.95, 95% CI 1.11-7.79). Our results suggest that dental caries incidence at an individual level can be associated with the experience of dental caries in primary teeth and Dentocult LB based on the survival models for the 5-year prospective data. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

    Directory of Open Access Journals (Sweden)

    Taysseer Sharaf

    2015-01-01

    Full Text Available Artificial neural network (ANN theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.

  13. Bayesian analysis of right censored survival time data | Abiodun ...

    African Journals Online (AJOL)

    We analyzed cancer data using Fully Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) simulation technique which allows the estimation of very complex and realistic models. The results show that sex and age are significant risk factors for dying from some selected cancers. The risk of dying from ...

  14. GDISC: a web portal for integrative analysis of gene-drug interaction for survival in cancer.

    Science.gov (United States)

    Spainhour, John Christian Givhan; Lim, Juho; Qiu, Peng

    2017-05-01

    Survival analysis has been applied to The Cancer Genome Atlas (TCGA) data. Although drug exposure records are available in TCGA, existing survival analyses typically did not consider drug exposure, partly due to naming inconsistencies in the data. We have spent extensive effort to standardize the drug exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of cancer patients. Using this strategy, we integrated gene copy number data, drug exposure data and patient survival data to infer gene-drug interactions that impact survival. The collection of all analyzed gene-drug interactions in 32 cancer types are organized and presented in a searchable web-portal called gene-drug Interaction for survival in cancer (GDISC). GDISC allows biologists and clinicians to interactively explore the gene-drug interactions identified in the context of TCGA, and discover interactions associated to their favorite cancer, drug and/or gene of interest. In addition, GDISC provides the standardized drug exposure data, which is a valuable resource for developing new methods for drug-specific analysis. GDISC is available at https://gdisc.bme.gatech.edu/. peng.qiu@bme.gatech.edu.

  15. In-season retail sales forecasting using survival models

    African Journals Online (AJOL)

    analysis has increasingly been used in non-traditional fields, including the manufacturing industry (Berry 2009). The general ..... concern, since sales v olumes are usually highly v olatile throughout the season. •. Sheer simplicity. •. Ease of understanding the metho d. •. Ease of calculation. •. F ull automation of calculation p.

  16. The role of LINE-1 methylation in predicting survival among colorectal cancer patients: a meta-analysis.

    Science.gov (United States)

    Ye, Ding; Jiang, Danjie; Li, Yingjun; Jin, Mingjuan; Chen, Kun

    2017-08-01

    The prognostic value of long interspersed nucleotide element-1 (LINE-1) methylation in patients with colorectal cancer (CRC) remains uncertain. We have therefore performed a meta-analysis to elucidate this issue. The PubMed and Web of Science databases were searched for studies published up to 30 June 2016 which reported on an association between LINE-1 methylation and overall survival (OS), disease-free survival (DFS), or cancer-specific survival (CSS) among CRC patients. The reference lists of the identified studies were also analyzed to identify additional eligible studies. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using the fixed-effects or the random-effects model. Stratification analysis and meta-regression analysis were performed to detect the source of heterogeneity. Analyses of sensitivity and publication bias were also carried out. Thirteen independent studies involving 3620 CRC patients were recruited to the meta-analysis. LINE-1 hypomethylation was found to be significantly associated with shorter OS (HR 2.92, 95% CI 2.20-3.88, p LINE-1 hypomethylation and OS or DFS, with the exception being CSS. Moreover, meta-regression analysis suggested that one of the contributors to between-study heterogeneity on the association between LINE-1 methylation and CSS was statistical methodology. The subgroup analysis suggested that the association in studies using the Cox model statistical method (HR 2.76, 95% CI 1.90-4.01, p LINE-1 methylation is significantly associated with the survival of CRC patients and that it could be a predictive factor for CRC prognosis.

  17. ANP AFFECTS CARDIAC REMODELING, FUNCTION, HEART FAILURE AND SURVIVAL IN A MOUSE MODEL OF DILATED CARDIOMYOPATHY

    OpenAIRE

    Wang, Dong; Gladysheva, Inna P.; Fan, Tai-Hwang M.; Sullivan, Ryan; Houng, Aiilyan K.; Reed, Guy L.

    2013-01-01

    Dilated cardiomyopathy is a frequent cause of heart failure and death. Atrial natriuretic peptide (ANP) is a biomarker of dilated cardiomyopathy, but there is controversy whether ANP modulates the development of heart failure. Therefore we examined whether ANP affects heart failure, cardiac remodeling, function and survival in a well-characterized, transgenic model of dilated cardiomyopathy. Mice with dilated cardiomyopathy with normal ANP levels survived longer than mice with partial ANP (p

  18. Modelling of the process of micromycetus survival in fruit and berry syrups

    Directory of Open Access Journals (Sweden)

    L. Osipova

    2017-06-01

    Full Text Available In order to develop methods for preserving fruit and berry syrup, which exclude the use of high-temperature sterilization and preservatives, the survival of spores of micromycetes (B. nivea molds in model media with different concentration of food osmotically active substances (sucrose, ethyl alcohol, citric acid at a certain concentration of lethal effects on microorganisms. It has been established that model media (juice based syrups from blueberries with a mass content of 4 % and 6 % alcohol, 50 % sucrose, 1 % and 2 % titrated acids, have a lethal effect on spores of B. nivea molds. The regression equation is obtained expressing the dependence of the amount of spores of B. nivea molds on the concentration of sucrose, acid, alcohol and the storage time of syrups. The form of the dependence and direction of the connection between the variables is established – a negative linear regression, which is expressed in the uniform decrease of the function. The estimation of quality of the received regression model is defined. The deviations of the calculated data from the data of the initial set are calculated. The proposed model has sufficient reliability, since the regression function is defined, interpreted and justified, and the estimation of the accuracy of the regression analysis meets the requirements.

  19. Análise de sobrevivência relacionada ao sexo, após esplenectomia, em modelo animal Post-splenectomy survival analysis related to gender in animal model

    Directory of Open Access Journals (Sweden)

    Luiz R. Alberti

    2007-06-01

    Full Text Available É sabido que a esplenectomia causa propensão a infecções, principalmente aquelas causadas por microorganismos capsulados. Essas complicações acompanham-se de maior mortalidade e conseqüente menor sobrevida dos indivíduos asplênicos. O objetivo do presente trabalho foi avaliar a sobrevida em relação ao sexo de ratos submetidos a esplenectomia total. Foram estudados 34 ratos, distribuídos em dois grupos, sendo o Grupo 1 (n=14 controle, animais submetidos apenas a laparotomia e o Grupo 2 (n=20 submetidos a esplenectomia total. Os animais de cada grupo foram distribuídos em dois subgrupos iguais, sendo o Subgrupo A composto por machos e o Subgrupo B por fêmeas. Os ratos foram acompanhados por um período máximo de noventa dias com vista à sua sobrevivência. Observou-se que a mortalidade após esplenectomia total foi de 80% no Subgrupo dos animais machos e de 30% no das fêmeas. Não houve óbitos no Grupo 1. Os ratos esplenectomizados apresentaram sobrevida inferior à das ratas esplenectomizadas (p = 0,034. De acordo com os resultados obtidos, a esplenectomia diminui a sobrevida de ratos, e as fêmeas murinas apresentam maior resistência à asplenia e conseqüentemente menor mortalidade do que os machos.It is well known that splenectomy increases the risk of infections, mainly those caused by capsulated bacteria. These complications are associated with greater mortality and lower survival rates in asplenic individuals. The objective of the present work was to assess the survival of rats submitted to total splenectomy. Thirty-four rats were divided into 2 groups: Group 1 (n = 14: control animals, submitted only to laparotomy; Group 2 (n = 20: animals submitted to splenectomy. Both groups were subdivided into 2 subgroups, namely, subgroup A, male rats, and subgroup B, female rats. The animals were followed during a 90-day period to assess their survival. The mortality of animals in Group 2 was 80% for males and 30% for females. No

  20. [A survival analysis approach to assess the association between maternal smoking during pregnancy and childhood obesity].

    Science.gov (United States)

    Suzuki, Kohta; Sato, Miri; Ando, Daisuke; Kondo, Naoki; Yamagata, Zentaro

    2012-08-01

    It has been suggested that maternal smoking during pregnancy has an effect on childhood obesity. We previously clarified the association between maternal lifestyle habits practiced during pregnancy, including smoking, and childhood obesity and overweight at 9-10 years of age. In this study, we aimed to demonstrate this association through survival analysis. This study was based on an on-going community-based prospective cohort study initiated in the fetal stage called Project Koshu. The study population comprised of the participants of Project Koshu, who were children born in a rural Japanese area between 1991 and 1999 and their mothers. In this project, maternal smoking status during pregnancy was collected through a questionnaire and childhood anthropometric data were measured at annual medical check-ups from 3 years of age to 9-10 years of age. Using these data, we performed a survival analysis using the Kaplan-Meier method to compare the cumulative rate of childhood obesity and overweight between those with mothers who smoked during pregnancy and those who did not. Subsequently, we calculated the hazard ratio (HR) of the effect of maternal smoking during pregnancy on childhood obesity using the Cox proportional hazard model. In the survival analysis of childhood obesity, we analyzed the data of 1428 children and their mothers (follow-up rate: 87.7%). Of these, 290 children (20.3%) became overweight and 92 children (6.4%) became obese between 3 years of age and 9-10 years of age. This shows that the cumulative rate of childhood obesity was significantly different between mothers with and without smoking habits (P obese between 3 years of age and 9-10 years of age. Maternal smoking during pregnancy was found to be associated with childhood obesity (HR, 2.0; 95% confidence interval (CI): 1.04-4.0). However, there was no significant association between maternal smoking during pregnancy and childhood overweight. Our results suggest that the effect of fetal

  1. Oral rehabilitation with dental implants in irradiated patients: a meta-analysis on implant survival.

    Science.gov (United States)

    Schiegnitz, E; Al-Nawas, B; Kämmerer, P W; Grötz, K A

    2014-04-01

    The aim of this comprehensive literature review is to provide recommendations and guidelines for dental implant therapy in patients with a history of radiation in the head and neck region. For the first time, a meta-analysis comparing the implant survival in irradiated and non-irradiated patients was performed. An extensive electronic search in the electronic databases of the National Library of Medicine was conducted for articles published between January 1990 and January 2013 to identify literature presenting survival data on the topic of dental implants in patients receiving radiotherapy for head and neck cancer. Review and meta-analysis were performed according to Preferred Reporting Items for Systematic Review and Meta-Analyses statement. For meta-analysis, only studies with a mean follow-up of at least 5 years were included. After screening 529 abstracts from the electronic database, we included 31 studies in qualitative and 8 in quantitative synthesis. The mean implant survival rate of all examined studies was 83 % (range, 34-100 %). Meta-analysis of the current literature (2007-2013) revealed no statistically significant difference in implant survival between non-irradiated native bone and irradiated native bone (odds ratio [OR], 1.44; confidence interval [CI], 0.67-3.1). In contrast, meta-analysis of the literature of the years 1990-2006 showed a significant difference in implant survival between non-irradiated and irradiated patients ([OR], 2.12; [CI], 1.69-2.65) with a higher implant survival in the non-irradiated bone. Meta-analysis of the implant survival regarding bone origin indicated a statistically significant higher implant survival in the irradiated native bone compared to the irradiated grafted bone ([OR], 1.82; [CI], 1.14-2.90). Within the limits of this meta-analytic approach to the literature, this study describes for the first time a comparable implant survival in non-irradiated and irradiated native bone in the current literature. Grafted

  2. Analysis of individual- and time-specific covariate effects on survival of Serinus serinus in north-eastern Spain

    Science.gov (United States)

    Conroy, M.J.; Senar, J.C.; Domenech, J.

    2002-01-01

    We developed models for the analysis of recapture data for 2678 serins (Serinus serinus) ringed in north-eastern Spain since 1985. We investigated several time- and individual-specific factors as potential predictors of overall mortality and dispersal patterns, and of gender and age differences in these patterns. Time-specific covariates included minimum daily temperature, days below freezing, and abundance of a strong competitor, siskins (Carduelis spinus) during winter, and maximum temperature and rainfall during summer. Individual covariates included body mass (i.e. body condition), and wing length (i.e. flying ability), and interactions between body mass and environmental factors. We found little support of a predictive relationship between environmental factors and survival, but good evidence of relationships between body mass and survival, especially for juveniles. Juvenile survival appears to vary in a curvilinear manner with increasing mass, suggesting that there may exist an optimal mass beyond which increases are detrimental. The mass-survival relationship does seem to be influenced by at least one environmental factor, namely the abundance of wintering siskins. When siskins are abundant, increases in body mass appear to relate strongly to increasing survival. When siskin numbers are average or low the relationship is largely reversed, suggesting that the presence of strong competition mitigates the otherwise largely negative aspects of greater body mass. Wing length in juveniles also appears to be related positively to survival, perhaps largely due to the influence of a few unusually large juveniles with adult-like survival. Further work is needed to test these relationships, ideally under experimentation.

  3. Using simulation to interpret a discrete time survival model in a complex biological system: fertility and lameness in dairy cows.

    Directory of Open Access Journals (Sweden)

    Christopher D Hudson

    Full Text Available The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period, PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd rather than individual level.

  4. Handling incomplete smoking history data in survival analysis.

    Science.gov (United States)

    Furukawa, Kyoji; Preston, Dale L; Misumi, Munechika; Cullings, Harry M

    2017-04-01

    While data are unavoidably missing or incomplete in most observational studies, consequences of mishandling such incompleteness in analysis are often overlooked. When time-varying information is collected irregularly and infrequently over a long period, even precisely obtained data may implicitly involve substantial incompleteness. Motivated by an analysis to quantitatively evaluate the effects of smoking and radiation on lung cancer risks among Japanese atomic-bomb survivors, we provide a unique application of multiple imputation to incompletely observed smoking histories under the assumption of missing at random. Predicting missing values for the age of smoking initiation and, given initiation, smoking intensity and cessation age, analyses can be based on complete, though partially imputed, smoking histories. A simulation study shows that multiple imputation appropriately conditioned on the outcome and other relevant variables can produce consistent estimates when data are missing at random. Our approach is particularly appealing in large cohort studies where a considerable amount of time-varying information is incomplete under a mechanism depending in a complex manner on other variables. In application to the motivating example, this approach is expected to reduce estimation bias that might be unavoidable in naive analyses, while keeping efficiency by retaining known information.

  5. Preoperative risk factors predict survival following cardiac retransplantation: analysis of the United Network for Organ Sharing database.

    Science.gov (United States)

    Belli, Erol; Leoni Moreno, Juan Carlos; Hosenpud, Jeffrey; Rawal, Bhupendra; Landolfo, Kevin

    2014-06-01

    The aim of our study was to identify preoperative risk factors affecting overall survival after cardiac retransplantation (ReTX) in a contemporary era. The United Network for Organ Sharing database was used to identify patients undergoing ReTX between 1995 and 2012. Of the total 28,464 primary transplants performed, 987 (3.5%) were retransplants. The primary outcome investigated was overall survival. The influence of preoperative donor and recipient characteristics on survival were then tested with univariate logistic regression and multivariate Cox regression models. Of 987 patients who underwent ReTX, median survival was 9 years. Estimated survival at 1, 3, 5, 10, and 15 years following retransplant was 80% (95% confidence interval [CI], 78%-83%), 70% (95% CI, 67%-73%), 64% (95% CI, 61%-67%), 47% (95% CI, 43%-51%), and 30% (95% CI, 25%-37%), respectively. Clinical predictors of survival using multivariable analysis included donor age (relative risk [RR], 1.14; P = .004), ischemic time > 4 hours (RR, 1.48; P = .004); preoperative support with extracorporeal membrane oxygenator (RR, 3.91; P risk of death compared with patients undergoing primary transplant only (RR, 1.27; 95% CI, 1.13-1.42; P < .001). Patients who undergo cardiac ReTX can expect to have a 1-year survival less than a patient undergoing primary transplant with an acceptable median overall survival. Both donor and recipient preoperative factors contribute to overall survival following cardiac ReTx. Donor characteristics include age of the donor and ischemic time. Recipient factors include the need for extracorporeal membrane oxygenator and the number of days between the first and second transplant. Optimal survival following cardiac ReTX can best be predicted by choosing patients who are farther out from their initial transplant, not dependent upon preoperative extracorporeal support, and by choosing donor hearts younger in age and those likely to have shorter ischemic times. Copyright © 2014 The

  6. Breastfeeding practices in a public health field practice area in Sri Lanka: a survival analysis

    Directory of Open Access Journals (Sweden)

    Agampodi Thilini C

    2007-10-01

    Full Text Available Abstract Background Exclusive breastfeeding up to the completion of the sixth month of age is the national infant feeding recommendation for Sri Lanka. The objective of the present study was to collect data on exclusive breastfeeding up to six months and to describe the association between exclusive breastfeeding and selected socio-demographic factors. Methods A clinic based cross-sectional study was conducted in the Medical Officer of Health area, Beruwala, Sri Lanka in June 2006. Mothers with infants aged 4 to 12 months, attending the 19 child welfare clinics in the area were included in the study. Infants with specific feeding problems (cleft lip and palate and primary lactose intolerance were excluded. Cluster sampling technique was used and consecutive infants fulfilling the inclusion criteria were enrolled. A total of 219 mothers participated in the study. The statistical tests used were survival analysis (Kaplan-Meier survival curves and Cox proportional Hazard model. Results All 219 mothers had initiated breastfeeding. The median duration of exclusive breastfeeding was four months (95% CI 3.75, 4.25. The rates of exclusive breastfeeding at 4 and 6 months were 61.6% (135/219 and 15.5% (24/155 respectively. Bivariate analysis showed that the Muslim ethnicity (p = 0.004, lower levels of parental education (p Conclusion The rate of breastfeeding initiation and exclusive breastfeeding up to the fourth month is very high in Medical Officer of Health area, Beruwala, Sri Lanka. However exclusive breastfeeding up to six months is still low and the prevalence of inappropriate feeding practices is high.

  7. 30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

    Science.gov (United States)

    Hassani, Sahar; Lindman, Anja Schou; Kristoffersen, Doris Tove; Tomic, Oliver; Helgeland, Jon

    2015-01-01

    The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator. Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in

  8. 30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

    Directory of Open Access Journals (Sweden)

    Sahar Hassani

    Full Text Available The Norwegian Knowledge Centre for the Health Services (NOKC reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no, as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator.Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI, stroke and hip fracture are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities

  9. Cigarette smoking is associated with adverse survival among women with ovarian cancer: Results from a pooled analysis of 19 studies.

    Science.gov (United States)

    Praestegaard, Camilla; Jensen, Allan; Jensen, Signe M; Nielsen, Thor S S; Webb, Penelope M; Nagle, Christina M; DeFazio, Anna; Høgdall, Estrid; Rossing, Mary Anne; Doherty, Jennifer A; Wicklund, Kristine G; Goodman, Marc T; Modugno, Francesmary; Moysich, Kirsten; Ness, Roberta B; Edwards, Robert; Matsuo, Keitaro; Hosono, Satoyo; Goode, Ellen L; Winham, Stacey J; Fridley, Brooke L; Cramer, Daniel W; Terry, Kathryn L; Schildkraut, Joellen M; Berchuck, Andrew; Bandera, Elisa V; Paddock, Lisa E; Massuger, Leon F; Wentzensen, Nicolas; Pharoah, Paul; Song, Honglin; Whittemore, Alice; McGuire, Valerie; Sieh, Weiva; Rothstein, Joseph; Anton-Culver, Hoda; Ziogas, Argyrios; Menon, Usha; Gayther, Simon A; Ramus, Susan J; Gentry-Maharaj, Alexandra; Wu, Anna H; Pearce, Celeste L; Pike, Malcolm; Lee, Alice W; Sutphen, Rebecca; Chang-Claude, Jenny; Risch, Harvey A; Kjaer, Susanne K

    2017-06-01

    Cigarette smoking is associated with an increased risk of developing mucinous ovarian tumors but whether it is associated with ovarian cancer survival overall or for the different histotypes is unestablished. Furthermore, it is unknown whether the association between cigarette smoking and survival differs according to strata of ovarian cancer stage at diagnosis. In a large pooled analysis, we evaluated the association between various measures of cigarette smoking and survival among women with epithelial ovarian cancer. We obtained data from 19 case-control studies in the Ovarian Cancer Association Consortium (OCAC), including 9,114 women diagnosed with ovarian cancer. Cox regression models were used to estimate adjusted study-specific hazard ratios (HRs), which were combined into pooled hazard ratios (pHR) with corresponding 95% confidence intervals (CIs) under random effects models. Overall, 5,149 (57%) women died during a median follow-up period of 7.0 years. Among women diagnosed with ovarian cancer, both current (pHR = 1.17, 95% CI: 1.08-1.28) and former smokers (pHR = 1.10, 95% CI: 1.02-1.18) had worse survival compared with never smoking women. In histotype-stratified analyses, associations were observed for mucinous (current smoking: pHR = 1.91, 95% CI: 1.01-3.65) and serous histotypes (current smoking: pHR = 1.11, 95% CI: 1.00-1.23; former smoking: pHR = 1.12, 95% CI: 1.04-1.20). Further, our results suggested that current smoking has a greater impact on survival among women with localized than disseminated disease. The identification of cigarette smoking as a modifiable factor associated with survival has potential clinical importance as a focus area to improve ovarian cancer prognosis. © 2017 UICC.

  10. The impact of psychosocial intervention on survival in cancer: a meta-analysis.

    Science.gov (United States)

    Fu, Wayne W; Popovic, Marko; Agarwal, Arnav; Milakovic, Milica; Fu, Terence S; McDonald, Rachel; Fu, Gordon; Lam, Michael; Chow, Ronald; Cheon, Stephanie; Pulenzas, Natalie; Lam, Henry; DeAngelis, Carlo; Chow, Edward

    2016-04-01

    The impact of psychosocial interventions on survival remains controversial in patients with cancer. A meta-analysis of the recent literature was conducted to evaluate the potential survival benefit associated with psychosocial interventions for cancer patients. MEDLINE, EMBASE, and Cochrane Central were searched from January 2004 to May 2015 for all randomized controlled trials (RCTs) that compared survival outcomes between cancer patients receiving a psychosocial intervention and those receiving other, or no interventions. Endpoints included one-, two-, and four-year overall survival. Subgroup analyses were performed to compare group-versus individually-delivered interventions, and to assess breast cancer-only trials. Of 5,080 identified articles, thirteen trials were included for analysis. There was a significant survival benefit for the intervention group at one year [risk ratio (RR) =0.82; 95% confidence interval (CI), 0.67-1.00; P=0.04] and two years (RR =0.86; 95% CI, 0.78-0.95; P=0.003). However, no significant difference was detected at four years (RR =0.94; 95% CI, 0.85-1.04; P=0.24). Among patients with breast cancer, there was a significant survival benefit of psychosocial interventions at one year (RR =0.59; 95% CI, 0.42-0.82; P=0.002), but no difference at two years (RR =0.82; 95% CI, 0.67-1.02; P=0.07) or four years (RR =0.95; 95% CI, 0.73-1.23; P=0.68). Group-delivered interventions had a significant survival benefit favouring the intervention group at one year (RR =0.57; 95% CI, 0.41-0.79; P=0.0008), but no difference at two years (RR =0.84; 95% CI, 0.68-1.02; P=0.08) or four years (RR =0.94; 95% CI, 0.75-1.20; P=0.64). Individually-delivered interventions had no significant survival benefit at one year (RR =0.92; 95% CI, 0.79-1.08; P=0.32), two years (RR =0.87; 95% CI, 0.75-1.00; P=0.05), or four years (RR =0.93; 95% CI, 0.84-1.04; P=0.21). For the main analysis and group-delivered treatments, psychosocial interventions demonstrated only short

  11. Revisit of 1997 TNM staging system--survival analysis of 1112 lung cancer patients in Taiwan.

    Science.gov (United States)

    Perng, Reury-Perng; Chen, Chih-Yi; Chang, Gee-Chen; Hsia, Te-Chun; Hsu, Nan-Yung; Tsai, Ying-Huang; Tsai, Chun-Ming; Yang, Chih-Hsin; Chen, Yuh-Min; Yu, Chong-Jen; Lee, Jen-Jyh; Hsu, Han-Shui; Yu, Chih-Teng; Kao, Eing-Long; Chiu, Chao-Hua

    2007-01-01

    There is neither a nation-wide nor a large-scale, multi-institutional lung cancer database available for stage-by-stage survival analysis in Taiwan at present. Using the data element provided by the International Association for the Study of Lung Cancer, the Taiwan Lung Cancer Society initiated a project to include native lung cancer patients into a global database. A total of 1112 Taiwan lung cancer patients treated in 7 medical centers were enrolled. In small cell lung cancer, patients with ipsilateral pleural effusion had a survival between those with locoregional disease alone and those with distant metastasis; however, the difference was not statistically significant (P = 0.204). In non-small cell lung cancer, tumor size had significant survival influence for patients as a whole (P < 0.001) but it did not support the further division of stage IA according to tumor size (P = 0.122). The survival was compatible in stage IIIB and IV patients and therefore, the survival impact of pleural effusion cannot be determined. In patients with pIIIA-N2 disease, those who had station 8 nodal metastasis had inferior survival (P = 0.020) and station 5 superior survival (P = 0.010). In patients with distant metastasis, bone, liver, or distant lymph node metastasis predicted an inferior survival (all P values < 0.05). The present study provides for comparison in this area a stage-by-stage reference for the survival of lung cancer patients. Some factors other than current TNM descriptors need to be further investigated in constructing the next version of the staging system.

  12. Survival trees: an alternative non-parametric multivariate technique for life history analysis.

    Science.gov (United States)

    De Rose, A; Pallara, A

    1997-01-01

    "In this paper an extension of tree-structured methodology to cover censored survival analysis is discussed.... The tree-shaped diagram...can be used to draw meaningful patterns of behaviour throughout the individual life history.... The fundamentals of tree methodology are outlined; [then] an application of the technique to real data from a survey on the progression to marriage among adult women in Italy is illustrated; [and] some comments are presented on the main advantages and problems related to tree-structured methodology for censored survival analysis." (EXCERPT)

  13. Predictability of survival models for waiting list and transplant patients: calculating LYFT.

    Science.gov (United States)

    Wolfe, R A; McCullough, K P; Leichtman, A B

    2009-07-01

    'Life years from transplant' (LYFT) is the extra years of life that a candidate can expect to achieve with a kidney transplant as compared to never receiving a kidney transplant at all. The LYFT component survival models (patient lifetimes with and without transplant, and graft lifetime) are comparable to or better predictors of long-term survival than are other predictive equations currently in use for organ allocation. Furthermore, these models are progressively more successful at predicting which of two patients will live longer as their medical characteristics (and thus predicted lifetimes) diverge. The C-statistics and the correlations for the three LYFT component equations have been validated using independent, nonoverlapping split-half random samples. Allocation policies based on these survival models could lead to substantial increases in the number of life years gained from the current donor pool.

  14. Jointly modeling longitudinal proportional data and survival times with an application to the quality of life data in a breast cancer trial.

    Science.gov (United States)

    Song, Hui; Peng, Yingwei; Tu, Dongsheng

    2017-04-01

    Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.

  15. Simple models based on gamma-glutamyl transpeptidase and platelets for predicting survival in hepatitis B-associated hepatocellular carcinoma.

    Science.gov (United States)

    Pang, Qing; Bi, Jian-Bin; Wang, Zhi-Xin; Xu, Xin-Sen; Qu, Kai; Miao, Run-Chen; Chen, Wei; Zhou, Yan-Yan; Liu, Chang

    2016-01-01

    Several hepatic cirrhosis-derived noninvasive models have been developed to predict the incidence and outcomes of hepatocellular carcinoma (HCC). We aimed to investigate the prognostic significance of the two novel established cirrhosis-associated models based on gamma-glutamyl transpeptidase (GGT) and platelets in hepatitis B-associated HCC. We retrospectively evaluated 182 HCC patients with positive hepatitis B surface antigen who received radical therapy at a single institution between 2002 and 2012. Laboratory data prior to operation were collected to calculate the GGT to platelets ratio (GPR) and the S-index. Predictive factors associated with overall survival and recurrence-free survival were assessed using log-rank test and multivariate Cox analysis. Additional analyses were performed after patients were stratified based on cirrhosis status, tumor size, therapy methods, and so forth, to investigate the prognostic significance in different subgroups. During a median follow-up time of 45.0 months, a total of 88 (48.4%) patients died and 79 (43.4%) patients recurred. The cut-off points for GPR and S-index in predicting death were determined to be 0.76 and 0.56, respectively. Compared with patients with a lower GPR, those with GPR ≥0.76 had a higher probability of cirrhosis and a larger tumor (both P<0.05). GPR and S-index were both found to be significantly associated with survival by univariate log-rank test. Multivariate analysis identified tumor size ≥5 and high level of GPR, but not high Barcelona Clinic Liver Cancer stage or S-index, as independent factors for predicting poor overall survival and recurrence-free survival. The GPR is an effective preoperative predictor for outcomes in hepatitis B-associated HCC.

  16. The Association between Phase Angle of Bioelectrical Impedance Analysis and Survival Time in Advanced Cancer Patients: Preliminary Study.

    Science.gov (United States)

    Lee, So Yeon; Lee, Yong Joo; Yang, Jung-Hwa; Kim, Chul-Min; Choi, Whan-Seok

    2014-09-01

    A frequent manifestation of advanced cancer patients is malnutrition, which is correlated with poor prognosis and high mortality. Bioelectrical impedance analysis (BIA) is an easy-to-use and non-invasive technique to evaluate changes in body composition and nutritional status. We investigated BIA-derived phase angle as a prognostic indicator for survival in advanced cancer patients. Twenty-eight patients treated at the hospice center of Seoul St. Mary's Hospital underwent BIA measurements from January, 2013 to May, 2013. We also evaluated palliative prognostic index (PPI) and palliative performance scale to compare with the prognostic value of phase angle. Cox's proportional hazard models were constructed to evaluate the prognostic effect of phase angle. The Kaplan Meier method was used to calculate survival. Using univariate Cox analysis, phase angle (hazard ratio [HR], 0.61/per degree increase; 95% confidence interval [CI], 0.42 to 0.89; P = 0.010), PPI (HR, 1.21; 95% CI, 1.00 to 1.47; P = 0.048) were found to be significantly associated with survival. Adjusting age, PPI, body mass index, phase angle significantly showed association with survival in multivariate analysis (HR, 0.64/per degree increase; 95% CI, 0.42 to 0.95; P = 0.028). Survival time of patients with phase angle ≥ 4.4° was longer than patients with phase angle < 4.4° (log rank, 6.208; P-value = 0.013). Our data suggest BIA-derived phase angle may serve as an independent prognostic indicator in advanced cancer patients.

  17. SURVIVAL ANALYSIS AND GROWTH OF Cordia trichotoma, BORAGINACEAE, LAMIALES, IN MATO GROSSO DO SUL STATE, BRAZIL

    Directory of Open Access Journals (Sweden)

    Sergio Luiz Salvadori

    2013-12-01

    Full Text Available http://dx.doi.org/10.5902/1980509812357The evaluation of a plant survival percentage and growth may reflect its competitive ability in plantcommunity. Cordia trichotoma is a common native tree in Mato Grosso do Sul State and one of the mostpromising for planting. This study monitored the survival percentage and growth of Cordia trichotomaunder different conditions such as weeding and receiving or not fertilization. The experiment started inSeptember 2008 and it was concluded in March 2010. The seeds collection and sowing were held in urbanarea of Mundo Novo Municipality and the area for permanent planting to measure seedlings survival andgrowth was set at Japorã Municipality, Fazenda Santa Clara. Seedlings were planted in two categories: theuse or not of fertilizer and crowing resulting in four distinct groups: block fertilizer bare earth (ATN, bareland block without fertilizer (BTN, fertilizer and crown block (AC and without fertilizer and crownedblock (BC. The results indicated high survival of Cordia trichotoma in the seedling transplant system from bed to bags. The BC block showed the highest percentage of survival, but the smaller increments in height.The AC, ATN and BTN blocks presented the same survival pattern and similar average growth. However,there may be differences in nutritional and chemical composition of the soil suggesting sector analysis forfuture studies.

  18. Interleukin-7 ameliorates immune dysfunction and improves survival in a 2-hit model of fungal sepsis.

    Science.gov (United States)

    Unsinger, Jacqueline; Burnham, Carey-Ann D; McDonough, Jacquelyn; Morre, Michel; Prakash, Priya S; Caldwell, Charles C; Dunne, W Michael; Hotchkiss, Richard S

    2012-08-15

    Secondary hospital-acquired fungal infections are common in critically-ill patients and mortality remains high despite antimicrobial therapy. Interleukin-7 (IL-7) is a potent immunotherapeutic agent that improves host immunity and has shown efficacy in bacterial and viral models of infection. This study examined the ability of IL-7, which is currently in multiple clinical trials (including hepatitis and human immunodeficiency virus), to improve survival in a clinically relevant 2-hit model of fungal sepsis. Mice underwent cecal ligation and puncture to induce peritonitis. Four days later, surviving mice had intravenous injection with Candida albicans. Following Candida infection, mice were treated with IL-7 or saline control. The effect of IL-7 on host immunity and survival was recorded. IL-7 ameliorated the loss of immune effector cells and increased lymphocyte functions, including activation, proliferation, expression of adhesion molecules, and interferon-γ production. These beneficial effects of IL-7 were associated with an increase in global immunity as reflected by an enhanced delayed type hypersensitivity response and a 1.7-fold improvement in survival. The present findings showing that IL-7 improves survival in fungal sepsis, together with its previously reported efficacy in bacterial and viral infectious models, further supports its use as a novel immunotherapeutic in sepsis.

  19. Microcomputer-assisted univariate survival data analysis using Kaplan-Meier life table estimators.

    Science.gov (United States)

    Campos-Filho, N; Franco, E L

    1988-01-01

    We describe a microcomputer program (KMSURV) for exploratory univariate statistical analysis of survival data which is directly applicable to the evaluation of clinical trials and to retrospective epidemiological studies of hospital registry-based data. The program calculates life-table-like information based on Kaplan-Meier's product-limit estimators of the survivorship function S(t) and provides summary measures of average survival times. In addition, two non-parametric tests for the comparison of survival distributions are performed. A report-quality, high resolution plot of the S(t) estimates for all groups being compared complements each set of analyses. KMSURV is not a simple adaptation of a mainframe statistical analysis package and, thus, it utilizes efficiently the interactive environment which is inherent in microcomputing.

  20. Cure fraction estimation from the mixture cure models for grouped survival data.

    Science.gov (United States)

    Yu, Binbing; Tiwari, Ram C; Cronin, Kathleen A; Feuer, Eric J

    2004-06-15

    Mixture cure models are usually used to model failure time data with long-term survivors. These models have been applied to grouped survival data. The models provide simultaneous estimates of the proportion of the patients cured from disease and the distribution of the survival times for uncured patients (latency distribution). However, a crucial issue with mixture cure models is the identifiability of the cure fraction and parameters of kernel distribution. Cure fraction estimates can be quite sensitive to the choice of latency distributions and length of follow-up time. In this paper, sensitivity of parameter estimates under semi-parametric model and several most commonly used parametric models, namely lognormal, loglogistic, Weibull and generalized Gamma distributions, is explored. The cure fraction estimates from the model with generalized Gamma distribution is found to be quite robust. A simulation study was carried out to examine the effect of follow-up time and latency distribution specification on cure fraction estimation. The cure models with generalized Gamma latency distribution are applied to the population-based survival data for several cancer sites from the Surveillance, Epidemiology and End Results (SEER) Program. Several cautions on the general use of cure model are advised. Copyright 2004 John Wiley & Sons, Ltd.

  1. HIV testing in the maternity ward and the start of breastfeeding: a survival analysis.

    Science.gov (United States)

    Possolli, Glaucia T; Carvalho, Márcia L de; Oliveira, Maria Inês C de

    2015-01-01

    The purpose of this study was to analyze the factors that influence of the time between birth and the beginning of breastfeeding, especially at the moment of the rapid HIV test results at hospital admission for delivery. Cohort study of 932 pregnant women who underwent rapid HIV test admitted in the hospital for delivery in Baby-Friendly Hospitals. The survival curves of time from birth to the first feeding were estimated by the Kaplan-Meier method and the joint effect of independent variables by the Cox model with a hierarchical analysis. As the survival curves were not homogeneous among the five hospitals, hindering the principle of proportionality of risks, the data were divided into two groups according to the median time of onset of breastfeeding at birth in women undergoing rapid HIV testing. Hospitals with median time to breastfeeding onset at birth of up to 60 min were considered as early breastfeeding onset and those with higher medians were considered as late breastfeeding onset at birth. Risk factors common to hospitals considered to be with early and late breastfeeding onset at birth were: cesarean section (RR=1.75 [95% CI: 1.38-2.22]; RR=3.83 [95% CI: 3.03-4.85]) and rapid test result after birth (RR=1.45 [95% CI: 1.12-1.89]; RR=1.65 [95% CI: 1.35-2.02]), respectively; and hospitals with late onset: starting prenatal care in the third trimester (RR=1.86 [95% CI: 1.16-2.97]). The onset of breastfeeding is postponed, even in Baby-Friendly Hospitals, when the results of the rapid HIV test requested in the maternity are not available at the time of delivery. Copyright © 2015 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  2. HIV testing in the maternity ward and the start of breastfeeding: a survival analysis

    Directory of Open Access Journals (Sweden)

    Glaucia T. Possolli

    2015-08-01

    Full Text Available OBJECTIVE: The purpose of this study was to analyze the factors that influence of the time between birth and the beginning of breastfeeding, especially at the moment of the rapid HIV test results at hospital admission for delivery.METHODS: Cohort study of 932 pregnant women who underwent rapid HIV test admitted in the hospital for delivery in Baby-Friendly Hospitals. The survival curves of time from birth to the first feeding were estimated by the Kaplan-Meier method and the joint effect of independent variables by the Cox model with a hierarchical analysis. As the survival curves were not homogeneous among the five hospitals, hindering the principle of proportionality of risks, the data were divided into two groups according to the median time of onset of breastfeeding at birth in women undergoing rapid HIV testing.RESULTS: Hospitals with median time to breastfeeding onset at birth of up to 60 min were considered as early breastfeeding onset and those with higher medians were considered as late breastfeeding onset at birth. Risk factors common to hospitals considered to be with early and late breastfeeding onset at birth were: cesarean section (RR = 1.75 [95% CI: 1.38-2.22]; RR = 3.83 [95% CI: 3.03-4.85] and rapid test result after birth (RR = 1.45 [95% CI: 1.12-1.89]; RR = 1.65 [95% CI: 1.35-2.02], respectively; and hospitals with late onset: starting prenatal care in the third trimester (RR = 1.86 [95% CI: 1.16-2.97].CONCLUSIONS: The onset of breastfeeding is postponed, even in Baby-Friendly Hospitals, when the results of the rapid HIV test requested in the maternity are not available at the time of delivery.

  3. Semiparametric maximum likelihood estimation in normal transformation models for bivariate survival data

    OpenAIRE

    Yi Li; Ross L. Prentice; Xihong Lin

    2008-01-01

    We consider a class of semiparametric normal transformation models for right-censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation ...

  4. Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Yu Jin; Kim, Eunji [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Kim, Hak Jae, E-mail: khjae@snu.ac.kr [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Wu, Hong-Gyun [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Cancer Research Institute, Seoul National University College of Medicine, Seoul (Korea, Republic of); Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul (Korea, Republic of); Yan, Jinchun [Department of Radiation Oncology, Dalian Medical University, Liaoning (China); Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai (China); Liu, Qin [The Wistar Institute, Philadelphia, Pennsylvania (United States); Patel, Shilpen [Department of Radiation Oncology, University of Washington Medical Center, Seattle, Washington (United States)

    2016-04-01

    Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwent macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.

  5. DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

    Science.gov (United States)

    Dehkordi, Azimeh N V; Kamali-Asl, Alireza; Wen, Ning; Mikkelsen, Tom; Chetty, Indrin J; Bagher-Ebadian, Hassan

    2017-09-01

    This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (K(trans) ) for Model 2; vp , K(trans) and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (K(trans) /kep ) of Model 3, and K(trans) of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS

  6. TP53 Mutations and Survival in Osteosarcoma Patients: A Meta-Analysis of Published Data

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2016-01-01

    Full Text Available Several research groups have examined the association between TP53 mutations and prognosis in human osteosarcoma. However, the results were controversial. The purpose of this study was to evaluate the prognostic value of TP53 mutations in osteosarcoma patients. A meta-analysis was conducted with all eligible studies which quantitatively evaluated the relationship between TP53 mutations and clinical outcome of osteosarcoma patients. Eight studies with a total of 210 patients with osteosarcoma were included in this meta-analysis. The risk ratio (RR with a 95% confidence interval (95% CI was calculated to assess the effect of TP53 mutations on 2-year overall survival. The quantitative synthesis of 8 published studies showed that TP53 mutations were associated with 2-year overall survival in osteosarcoma patients. These data suggested that TP53 mutations had an unfavorable impact on 2-year overall survival when compared to the counterparts with wild type (WT TP53 (RR: 1.79; 95% CI: 1.12 to 2.84; P=0.01; I2=0%. There was no between-study heterogeneity. TP53 mutations are an effective prognostic marker for survival of patients with osteosarcoma. However, further large-scale prospective trials should be performed to clarify the prognostic value of TP53 mutations on 3- or 5-year survival in osteosarcoma patients.

  7. Integrated population modeling reveals the impact of climate on the survival of juvenile emperor penguins.

    Science.gov (United States)

    Abadi, Fitsum; Barbraud, Christophe; Gimenez, Olivier

    2017-03-01

    Early-life demographic traits are poorly known, impeding our understanding of population processes and sensitivity to climate change. Survival of immature individuals is a critical component of population dynamics and recruitment in particular. However, obtaining reliable estimates of juvenile survival (i.e., from independence to first year) remains challenging, as immatures are often difficult to observe and to monitor individually in the field. This is particularly acute for seabirds, in which juveniles stay at sea and remain undetectable for several years. In this work, we developed a Bayesian integrated population model to estimate the juvenile survival of emperor penguins (Aptenodytes forsteri), and other demographic parameters including adult survival and fecundity of the species. Using this statistical method, we simultaneously analyzed capture-recapture data of adults, the annual number of breeding females, and the number of fledglings of emperor penguins collected at Dumont d'Urville, Antarctica, for the period 1971-1998. We also assessed how climate covariates known to affect the species foraging habitats and prey [southern annular mode (SAM), sea ice concentration (SIC)] affect juvenile survival. Our analyses revealed that there was a strong evidence for the positive effect of SAM during the rearing period (SAMR) on juvenile survival. Our findings suggest that this large-scale climate index affects juvenile emperor penguins body condition and survival through its influence on wind patterns, fast ice extent, and distance to open water. Estimating the influence of environmental covariates on juvenile survival is of major importance to understand the impacts of climate variability and change on the population dynamics of emperor penguins and seabirds in general and to make robust predictions on the impact of climate change on marine predators. © 2016 John Wiley & Sons Ltd.

  8. External validation of a 5-year survival prediction model after elective abdominal aortic aneurysm repair.

    Science.gov (United States)

    DeMartino, Randall R; Huang, Ying; Mandrekar, Jay; Goodney, Philip P; Oderich, Gustavo S; Kalra, Manju; Bower, Thomas C; Cronenwett, Jack L; Gloviczki, Peter

    2017-08-11

    The benefit of prophylactic repair of abdominal aortic aneurysms (AAAs) is based on the risk of rupture exceeding the risk of death from other comorbidities. The purpose of this study was to validate a 5-year survival prediction model for patients undergoing elective repair of asymptomatic AAA .05 indicating goodness of fit). Across different populations of patients, assessment of age and level of cardiac, pulmonary, and renal disease can accurately predict 5-year survival in patients with AAA <6.5 cm undergoing repair. This risk prediction model is a valid method to assess mortality risk in determining potential overall survival benefit from elective AAA repair. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

  9. Modeling the survival responses of a multi-component biofilm to environmental stress

    Science.gov (United States)

    Carles Brangarí, Albert; Manzoni, Stefano; Sanchez-Vila, Xavier; Fernàndez-Garcia, Daniel

    2017-04-01

    Biofilms are consortia of microorganisms embedded in self-produced matrices of biopolymers. The survival of such communities depends on their capacity to improve the environmental conditions of their habitat by mitigating, or even benefitting from some adverse external factors. The mechanisms by which the microbial habitat is regulated remain mostly unknown. However, many studies have reported physiological responses to environmental stresses that include the release of extracellular polymeric substances (EPS) and the induction of a dormancy state. A sound understanding of these capacities is required to enhance the knowledge of the microbial dynamics in soils and its potential role in the carbon cycle, with significant implications for the degradation of contaminants and the emission of greenhouse gases, among others. We present a numerical analysis of the dynamics of soil microbes and their responses to environmental stresses. The conceptual model considers a multi-component heterotrophic biofilm made up of active cells, dormant cells, EPS, and extracellular enzymes. Biofilm distribution and properties are defined at the pore-scale and used to determine nutrient availability and water saturation via feedbacks of biofilm on soil hydraulic properties. The pore space micro-habitat is modeled as a simplified pore-network of cylindrical tubes in which biofilms proliferate. Microbial compartments and most of the carbon fluxes are defined at the bulk level. Microbial processes include the synthesis, decay and detachment of biomass, the activation/deactivation of cells, and the release and reutilization of EPS. Results suggest that the release of EPS and the capacity to enter a dormant state offer clear evolutionary advantages in scenarios characterized by environmental stress. On the contrary, when the conditions are favorable, the diversion of carbon into the production of the aforementioned survival mechanisms does not confer any additional benefit and the population

  10. Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.

    Science.gov (United States)

    Ding, Jimin; Wang, Jane-Ling

    2008-06-01

    In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients.

  11. It's Deja Vu All over Again: Using Multiple-Spell Discrete-Time Survival Analysis.

    Science.gov (United States)

    Willett, John B.; Singer, Judith D.

    1995-01-01

    The multiple-spell discrete-time survival analysis method is introduced and illustrated using longitudinal data on exit from and reentry into the teaching profession. The method is applicable to many educational problems involving the sequential occurrence of disparate events or episodes. (SLD)

  12. Mortality and survival in systemic sclerosis: systematic review and meta-analysis.

    Science.gov (United States)

    Rubio-Rivas, Manuel; Royo, Cristina; Simeón, Carmen Pilar; Corbella, Xavier; Fonollosa, Vicent

    2014-10-01

    To determine the mortality, survival, and causes of death in patients with systemic sclerosis (SSc) through a meta-analysis of the observational studies published up to 2013. We performed a systematic review and meta-analysis of the observational studies in patients with SSc and mortality data from entire cohorts published in MEDLINE and SCOPUS up to July 2013. A total of 17 studies were included in the mortality meta-analysis from 1964 to 2005 (mid-cohort years), with data from 9239 patients. The overall SMR was 2.72 (95% CI: 1.93-3.83). A total of 43 studies have been included in the survival meta-analysis, reporting data from 13,529 patients. Cumulative survival from onset (first Raynaud's symptom) has been estimated at 87.6% at 5 years and 74.2% at 10 years, from onset (non-Raynaud's first symptom) 84.1% at 5 years and 75.5% at 10 years, and from diagnosis 74.9% at 5 years and 62.5% at 10 years. Pulmonary involvement represented the main cause of death. SSc presents a larger mortality than general population (SMR = 2.72). Cumulative survival from diagnosis has been estimated at 74.9% at 5 years and 62.5% at 10 years. Pulmonary involvement represented the main cause of death. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. When will I succeed in my first-year diploma? Survival analysis in Dutch higher education

    NARCIS (Netherlands)

    Bruinsma, Marjon; Jansen, Ellen P. W. A.

    2009-01-01

    The goal of this study was to illustrate survival analysis with higher education data and gain insight into a limited set of factors that predict when students passed their first-year examination at a Dutch university. Study participants consisted of 565 first-year students in four departments. Data

  14. Predicting Secondary School Dropout among South African Adolescents: A Survival Analysis Approach

    Science.gov (United States)

    Weybright, Elizabeth H.; Caldwell, Linda L.; Xie, Hui; Wegner, Lisa; Smith, Edward A.

    2017-01-01

    Education is one of the strongest predictors of health worldwide. In South Africa, school dropout is a crisis where by Grade 12, only 52% of the age appropriate population remain enrolled. Survival analysis was used to identify the risk of dropping out of secondary school for male and female adolescents and examine the influence of substance use…

  15. Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia

    Directory of Open Access Journals (Sweden)

    Shang-Yi Lee

    2014-11-01

    Conclusion: Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time.

  16. CNS involvement and treatment with interferon-α are independent prognostic factors in Erdheim-Chester disease: a multicenter survival analysis of 53 patients.

    Science.gov (United States)

    Arnaud, Laurent; Hervier, Baptiste; Néel, Antoine; Hamidou, Mohamed A; Kahn, Jean-Emmanuel; Wechsler, Bertrand; Pérez-Pastor, Gemma; Blomberg, Bjørn; Fuzibet, Jean-Gabriel; Dubourguet, François; Marinho, António; Magnette, Catherine; Noel, Violaine; Pavic, Michel; Casper, Jochen; Beucher, Anne-Bérangère; Costedoat-Chalumeau, Nathalie; Aaron, Laurent; Salvatierra, Juan; Graux, Carlos; Cacoub, Patrice; Delcey, Véronique; Dechant, Claudia; Bindi, Pascal; Herbaut, Christiane; Graziani, Giorgio; Amoura, Zahir; Haroche, Julien

    2011-03-10

    Erdheim-Chester disease (ECD) is a rare form of non-Langerhans histiocytosis, with noncodified therapeutic management and high mortality. No treatment has yet been shown to improve survival in these patients. We conducted a multicenter prospective observational cohort study to assess whether extraskeletal manifestations and interferon-α treatment would influence survival in a large cohort of ECD patients. To achieve this goal, we thoroughly analyzed the clinical presentation of 53 patients with biopsy-proven ECD, and we performed a survival analysis using Cox proportional hazard model. Fifty-three patients (39 men and 14 women) with biopsy-proven ECD were followed up between November 1981 and November 2010. Forty-six patients (87%) received interferon-α and/or PEGylated interferon-α. Multivariate survival analysis using Cox proportional hazard model revealed that central nervous system involvement was an independent predictor of death (hazard ratio = 2.51; 95% confidence interval, 1.28-5.52; P = .006) in our cohort. Conversely, treatment with interferon-α was identified as an independent predictor of survival (hazard ratio = 0.32; 95% confidence interval, 0.14-0.70; P = .006). Although definitive confirmation would require a randomized controlled trial, these results suggest that interferon-α improves survival in ECD patients. This may be seen as a significant advance, as it is the first time a treatment is shown to improve survival in this multisystemic disease with high mortality.

  17. SNP-SNP interaction analysis of NF-κB signaling pathway on breast cancer survival

    DEFF Research Database (Denmark)

    Jamshidi, Maral; Fagerholm, Rainer; Khan, Sofia

    2015-01-01

    , in an extensive dataset (n = 30,431) from the Breast Cancer Association Consortium, we investigated the association of 917 SNPs in 75 genes in the NF-κB pathway with breast cancer prognosis. We explored SNP-SNP interactions on survival using the likelihood-ratio test comparing multivariate Cox' regression models...

  18. Impact of Resection Margin Distance on Survival of Pancreatic Cancer: A Systematic Review and Meta-Analysis.

    Science.gov (United States)

    Kim, Kyung Su; Kwon, Jeanny; Kim, Kyubo; Chie, Eui Kyu

    2017-07-01

    While curative resection is the only chance of cure in pancreatic cancer, controversies exist about the impact of surgical margin status on survival. Non-standardized pathologic report and different criteria on the R1 status made it difficult to implicate adjuvant therapy after resection based on the margin status. We evaluated the influence of resection margins on survival by meta-analysis. We thoroughly searched electronic databases of PubMed, EMBASE, and Cochrane Library. We included studies reporting survival outcomes with different margin status: involved margin (R0 mm), margin clearance with ≤ 1 mm (R0-1 mm), and margin with > 1 mm (R>1 mm). Hazard ratio (HR) for overall survival was extracted, and a random-effects model was used for pooled analysis. A total of eight retrospective studies involving 1,932 patients were included. Pooled HR for overall survival showed that patients with R>1 mm had reduced risk of death than those with R0-1 mm (HR, 0.74; 95% confidence interval [CI], 0.61 to 0.88; p=0.001). In addition, patients with R0-1 mm had reduced risk of death than those with R0 mm (HR, 0.81; 95% CI, 0.72 to 0.91; p < 0.001). There was no heterogeneity between the included studies (I(2) index, 42% and 0%; p=0.10 and p=0.82, respectively). Our results suggest that stratification of the patients based on margin status is warranted in the clinical trials assessing the role of adjuvant treatment for pancreatic cancer.

  19. AIR Model Preflight Analysis

    Science.gov (United States)

    Tai, H.; Wilson, J. W.; Maiden, D. L.

    2003-01-01

    The atmospheric ionizing radiation (AIR) ER-2 preflight analysis, one of the first attempts to obtain a relatively complete measurement set of the high-altitude radiation level environment, is described in this paper. The primary thrust is to characterize the atmospheric radiation and to define dose levels at high-altitude flight. A secondary thrust is to develop and validate dosimetric techniques and monitoring devices for protecting aircrews. With a few chosen routes, we can measure the experimental results and validate the AIR model predictions. Eventually, as more measurements are made, we gain more understanding about the hazardous radiation environment and acquire more confidence in the prediction models.

  20. Modeling Hierarchically Clustered Longitudinal Survival Processes with Applications to Child Mortality and Maternal Health

    Directory of Open Access Journals (Sweden)

    Kuate-Defo, Bathélémy

    2001-01-01

    Full Text Available EnglishThis paper merges two parallel developments since the 1970s of newstatistical tools for data analysis: statistical methods known as hazard models that are used foranalyzing event-duration data and statistical methods for analyzing hierarchically clustered dataknown as multilevel models. These developments have rarely been integrated in research practice andthe formalization and estimation of models for hierarchically clustered survival data remain largelyuncharted. I attempt to fill some of this gap and demonstrate the merits of formulating and estimatingmultilevel hazard models with longitudinal data.FrenchCette étude intègre deux approches statistiques de pointe d'analyse des donnéesquantitatives depuis les années 70: les méthodes statistiques d'analyse desdonnées biographiques ou méthodes de survie et les méthodes statistiquesd'analyse des données hiérarchiques ou méthodes multi-niveaux. Ces deuxapproches ont été très peu mis en symbiose dans la pratique de recherche et parconséquent, la formulation et l'estimation des modèles appropriés aux donnéeslongitudinales et hiérarchiquement nichées demeure essentiellement un champd'investigation vierge. J'essaye de combler ce vide et j'utilise des données réellesen santé publique pour démontrer les mérites et contextes de formulation etd'estimation des modèles multi-niveaux et multi-états des données biographiqueset longitudinales.

  1. Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer.

    Directory of Open Access Journals (Sweden)

    Gianluca Ascolani

    2015-05-01

    Full Text Available Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET. In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.

  2. Adjuvant radiotherapy improves overall survival in patients with resected gastric adenocarcinoma: A National Cancer Data Base analysis.

    Science.gov (United States)

    Stumpf, Priscilla K; Amini, Arya; Jones, Bernard L; Koshy, Matthew; Sher, David J; Lieu, Christopher H; Schefter, Tracey E; Goodman, Karyn A; Rusthoven, Chad G

    2017-09-01

    For patients with resectable gastric adenocarcinoma, perioperative chemotherapy and adjuvant chemoradiotherapy (CRT) are considered standard options. In the current study, the authors used the National Cancer Data Base to compare overall survival (OS) between these regimens. Patients who underwent gastrectomy for nonmetastatic gastric adenocarcinoma from 2004 through 2012 were divided into those treated with perioperative chemotherapy without RT versus those treated with adjuvant CRT. Survival was estimated and compared using univariate and multivariate models adjusted for patient and tumor characteristics, surgical margin status, and the number of lymph nodes examined. Subset analyses were performed for factors chosen a priori, and potential interactions between treatment and covariates were assessed. A total of 3656 eligible patients were identified, 52% of whom underwent perioperative chemotherapy and 48% of whom received postoperative CRT. The median follow-up was 47 months, and the median age of the patients was 62 years. Analysis of the entire cohort demonstrated improved OS with adjuvant RT on both univariate (median of 51 months vs 42 months; P = .013) and multivariate (hazard ratio, 0.874; 95% confidence interval, 0.790-0.967 [P = .009]) analyses. Propensity score-matched analysis also demonstrated improved OS with adjuvant RT (median of 49 months vs 39 months; P = .033). On subset analysis, a significant interaction was observed between the survival impact of adjuvant RT and surgical margins, with a greater benefit of RT noted among patients with surgical margin-positive disease (hazard ratio with RT: 0.650 vs 0.952; P for interaction Cancer Data Base analysis, the use of adjuvant RT in addition to chemotherapy was associated with a significant OS advantage for patients with resected gastric cancer. The survival advantage observed with adjuvant CRT was most pronounced among patients with positive surgical margins. Cancer 2017;123:3402-9. © 2017 American

  3. Survival Benefit of Adjuvant Chemoradiotherapy in Patients With Ampulla of Vater Cancer: A Systematic Review and Meta-analysis.

    Science.gov (United States)

    Kwon, Jeanny; Kim, Byoung Hyuck; Kim, Kyubo; Chie, Eui Kyu; Ha, Sung W

    2015-07-01

    We conducted a systematic review and meta-analysis focusing on the impact of adjuvant radiotherapy (RT) on overall survival (OS) in ampulla of Vater (AoV) cancer. The adjuvant treatment for AoV cancer is a subject of controversy without convincing evidence from randomized study. A comprehensive search was performed in the databases of EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to July 2014. We included studies, which compared survival between patients with or without adjuvant RT after curative surgery solely for AoV cancer. Hazard ratio (HR) for OS was extracted, and a random-effects model was used for pooled analysis. Ten retrospective studies including 3361 patients met all inclusion criteria and were included for the final meta-analysis. Adjuvant RT was delivered with concurrent chemotherapy, mostly 5-fluorouracil, in all institutional studies. Generally, adjuvant RT groups included more patients with locally advanced disease or lymph node metastasis than did the surgery alone groups. The pooled results demonstrated that adjuvant RT significantly reduced the risk of death (HR = 0.75; P = 0.01). Exploratory analyses showed that patients with lymph node metastasis (HR = 0.52; P = 0.001) and locally advanced disease (HR = 0.42; P = 0.001) may also have survival benefit from adjuvant RT. No clear evidence of publication bias was found. This is the first meta-analysis evaluating the role of adjuvant RT in AoV cancer. Our results suggest the potential for survival benefit of adjuvant chemoradiotherapy. Further studies, preferably randomized clinical trials, are needed to confirm our results.

  4. Survival Analysis of 1,742 Patients with Stage IV Non-small Cell Lung Cancer

    Directory of Open Access Journals (Sweden)

    Hong PENG

    2011-04-01

    Full Text Available Background and objective At present non-small cell lung cancer (NSCLC is still the leading cause of death induced by cancer. The aim of this study is to investigate the prognostic factors of advanced NSCLC. Methods Total 1,742 cases of stage IV NSCLC data from Jan 4, 2000 to Dec 25, 2008 in Shanghai Chest Hospital were collected, confirmed by pathological examinations. Analysis was made to observe the impact of treatment on prognosis in gender, age, smoking history, pathology, classification, clinical TNM stage. Survival rate, survival difference were evaluated by Kaplan-Meire method and Logrank test respectively. The prognosis were analyzed by Cox multivariate regression. Results The median survival time of 1,742 patients was 10.0 months (9.5 months-10.5 months. One, two, three, four, and five-year survival rates were 44%, 22%, 13%, 9%, 6% respectively. The median survivals of single or multiple metastasis were 11 months vs 7 months (P < 0.001. Survival time were different in metastasic organs, with the median survival time as follows: lung for about 12 months (11.0 months-12.9 months, bone for 9 months (8.3 months-9.6 months, brain for 8 months (6.8 months-9.1 months, liver, adrenal gland, distannt lymph node metastasis for 5 months (3.8 months-6.1 months, and subcutaneous for 3 months (1.7 months-4.3 months. The median survival times of adenocarcinoma (n=1,086, 62% and squamous cell carcinoma cases (n=305, 17.5% were 12 months vs 8 months (P < 0.001. The median survival time of chemotherapy and best supportive care were 11 months vs 6 months (P < 0.001; the median survival times of with and without radiotherapy were 11 months vs 9 months (P=0.017. Conclusion Gender, age, gross type, pathological type, clinical T stage, N stage, numbers of metastatic organ, smoking history, treatment of advanced non-small cell lung cancer were independent prognostic factors.

  5. Post-surgery radiation in early breast cancer: survival analysis of registry data

    OpenAIRE

    Vinh-Hung, Vincent; BURZYKOWSKI, Tomasz; Van de Steene, Jan; Storme, Guy; Soete, Guy

    2002-01-01

    BACKGROUND AND PURPOSE: Overviews of randomized trials have shown a small survival advantage with post-surgery radiation in early breast cancer. The present study attempts to extend this observation through a systematic analysis of population data.Materials and METHODS: This retrospective cohort study used the Surveillance, Epidemiology, and End Results (SEER) data on 83,776 women with breast cancer diagnosed between 1988 and 1997, stage T1-T2, node negative or node positive. The analysis was...

  6. Mitochondrial-Based Treatments that Prevent Post-Traumatic Osteoarthritis in a Translational Large Animal Intraarticular Fracture Survival Model

    Science.gov (United States)

    2016-09-01

    Animal Intraarticular Fracture Survival Model PRINCIPAL INVESTIGATOR: James A. Martin, PhD CONTRACTING ORGANIZATION: University of Iowa Iowa City, IA...Post-Traumatic Osteoarthritis in a Translational Large Animal Intraarticular Fracture Survival Model 5b. GRANT NUMBER W81XWH-11-1-0583 5c...traumatic osteoarthritis, large animal model, oxidative stress, mitochondria, mechanotransduction, amobarbital, n-acetyl cysteine 16. SECURITY

  7. Survival in patients with primary Dermatofibrosarcoma Protuberans: National Cancer Data Base analysis.

    Science.gov (United States)

    Trofymenko, Oleksandr; Bordeaux, Jeremy S; Zeitouni, Nathalie C

    2017-11-23

    The predictors of mortality, second surgery, and postoperative radiation therapy for treating Dermatofibrosarcoma protuberans (DFSP) are not well described. We sought to determine the impact of patient demographics, tumor characteristics, and treatment site and modality on survival after primary DFSP. A retrospective analysis of data from the National Cancer Data Base program was performed for patients diagnosed with DFSP from 2003 to 2012. A total of 5249 cases were identified. Of these, 3.1% of patients died during an average of 51.4 months of follow up. After adjusting for relevant factors, uninsured and/or Medicaid/Medicare insurance, anaplastic histology, and positive postoperative margins predicted mortality, while treatment at Integrated Network Cancer programs predicted survival (P data was not cancer-specific. Better understanding of factors affecting survival outcomes may help improve management of DFSP and delineate other potential causes of increased morbidity and mortality. Copyright © 2017. Published by Elsevier Inc.

  8. Role of adipose-derived stromal cells in pedicle skin flap survival in experimental animal models

    Science.gov (United States)

    Foroglou, Pericles; Karathanasis, Vasileios; Demiri, Efterpi; Koliakos, George; Papadakis, Marios

    2016-01-01

    The use of skin flaps in reconstructive surgery is the first-line surgical treatment for the reconstruction of skin defects and is essentially considered the starting point of plastic surgery. Despite their excellent usability, their application includes general surgical risks or possible complications, the primary and most common is necrosis of the flap. To improve flap survival, researchers have used different methods, including the use of adipose-derived stem cells, with significant positive results. In our research we will report the use of adipose-derived stem cells in pedicle skin flap survival based on current literature on various experimental models in animals. PMID:27022440

  9. Cox Regression and Parametric Models: Comparison of How They Determine Factors Influencing Survival of Patients with Non-Small Cell Lung Carcinoma

    Science.gov (United States)

    Khaksar, Elahe; Askarishahi, Mohsen; Hekmatimoghaddam, Seyedhossein; Vahedian-Ardakani, Hassanali

    2017-12-29

    Background and objectives: The present study of survival rate of patients with non-small cell carcinoma (NSCLC) compared the efficiency of Cox semi-parametric vs. parametric models in determination of influencing factors. Methods: In this retrospective cohort study, data were gathered from 190 patients with a confirmed diagnosis of NSCLC referred to Shahid Sadoughi and Shohadaye Kargar Hospitals in Yazd, Iran during 2005 to 2014. To identify and compare factors influencing the survival rate, a Cox semi-parametric model was fitted to the data. Data analysis was performed using the R software version R3.3.1, and the significance level was set at 0.05. Results: The average age was 64.5 years. About 40% of patients had stage 4 disease. The median survival was 8 months. After comparing the models, the more efficient was the log-normal distribution (AIC=889.3829), with which disease stage, type of therapy, and age were significant factors. Among the different types of therapy, chemotherapy and radiotherapy yielded higher survival rates, and increased age was associated with lower survival. Conclusion: The most efficient model was a log-normal model. Implementation of optimal therapies at early stages can improve the survival of patients. Creative Commons Attribution License

  10. Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data

    Science.gov (United States)

    Li, Yi; Prentice, Ross L.; Lin, Xihong

    2008-01-01

    SUMMARY We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation parameter of the semiparametric normal transformation model, which characterizes the bivariate dependence of bivariate survival outcomes. In addition, a simple positive-mass-redistribution algorithm can be used to implement the estimation procedures. Since the likelihood function involves infinite-dimensional parameters, the empirical process theory is utilized to study the asymptotic properties of the proposed estimators, which are shown to be consistent, asymptotically normal and semiparametric efficient. A simple estimator for the variance of the estimates is also derived. The finite sample performance is evaluated via extensive simulations. PMID:19079778

  11. Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data.

    Science.gov (United States)

    Li, Yi; Prentice, Ross L; Lin, Xihong

    2008-12-01

    We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation parameter of the semiparametric normal transformation model, which characterizes the bivariate dependence of bivariate survival outcomes. In addition, a simple positive-mass-redistribution algorithm can be used to implement the estimation procedures. Since the likelihood function involves infinite-dimensional parameters, the empirical process theory is utilized to study the asymptotic properties of the proposed estimators, which are shown to be consistent, asymptotically normal and semiparametric efficient. A simple estimator for the variance of the estimates is also derived. The finite sample performance is evaluated via extensive simulations.

  12. Impact of Interstitial Pneumonia on the Survival and Risk Factors Analysis of Patients with Hematological Malignancy

    Directory of Open Access Journals (Sweden)

    Wei-Liang Chen

    2013-01-01

    Full Text Available Background. The emergence of interstitial pneumonia (IP in patients with hematological malignancy (HM is becoming a challenging scenario in current practice. However, detailed characterization and investigation of outcomes and risk factors on survival have not been addressed. Methods. We conducted a retrospective study of 42,584 cancer patients covering the period between 1996 and 2008 using the institutional cancer registry system. Among 816 HM patients, 61 patients with IP were recognized. The clinical features, laboratory results, and histological types were studied to determine the impact of IP on survival and identify the profile of prognostic factors. Results. HM patients with IP showed a significant worse survival than those without IP in the 5-year overall survival (P=0.027. The overall survival showed no significant difference between infectious pneumonia and noninfectious interstitial pneumonia (IIP versus nIIP (P=0.323. In a multivariate Cox regression model, leukocyte and platelet count were associated with increased risk of death. Conclusions. The occurrence of IP in HM patients is associated with increased mortality. Of interest, nIIP is a prognostic indicator in patients with lymphoma but not in patients with leukemia. However, aggressive management of IP in patients with HM is strongly advised, and further prospective survey is warranted.

  13. An approach to the drone fleet survivability assessment based on a stochastic continues-time model

    Science.gov (United States)

    Kharchenko, Vyacheslav; Fesenko, Herman; Doukas, Nikos

    2017-09-01

    An approach and the algorithm to the drone fleet survivability assessment based on a stochastic continues-time model are proposed. The input data are the number of the drones, the drone fleet redundancy coefficient, the drone stability and restoration rate, the limit deviation from the norms of the drone fleet recovery, the drone fleet operational availability coefficient, the probability of the drone failure-free operation, time needed for performing the required tasks by the drone fleet. The ways for improving the recoverable drone fleet survivability taking into account amazing factors of system accident are suggested. Dependencies of the drone fleet survivability rate both on the drone stability and the number of the drones are analysed.

  14. Arthritis and the Risk of Falling Into Poverty: A Survival Analysis Using Australian Data.

    Science.gov (United States)

    Callander, Emily J; Schofield, Deborah J

    2016-01-01

    Low income is known to be associated with having arthritis. However, no longitudinal studies have documented the relationship between developing arthritis and falling into poverty. The purpose of this study was to evaluate Australians who developed arthritis to determine if they had an elevated risk of falling into poverty. Survival analysis using Cox regression models was applied to nationally representative, longitudinal survey data obtained between January 1, 2007 and December 31, 2012 from Australian adults who were ages 21 years and older in 2007. The hazard ratio for falling into income poverty was 1.08 (95% confidence interval [95% CI] 1.06-1.09) in women who were diagnosed as having arthritis and 1.15 (95% CI 1.13-1.16) in men who were diagnosed as having arthritis, as compared to those who were never diagnosed as having arthritis. The hazard ratio for falling into multidimensional poverty was 1.15 (95% CI 1.14-1.17) in women who were diagnosed as having arthritis and 1.88 (95% CI 1.85-1.91) in men who were diagnosed as having arthritis. Developing arthritis increases the risk of falling into income poverty and multidimensional poverty. The risk of multidimensional poverty is greater than the risk of income poverty. Given the high prevalence of arthritis, the condition is likely an overlooked driver of poverty. © 2016, American College of Rheumatology.

  15. Survival Analysis of Faculty Retention and Promotion in the Social Sciences by Gender.

    Directory of Open Access Journals (Sweden)

    Janet M Box-Steffensmeier

    Full Text Available Recruitment and retention of talent is central to the research performance of universities. Existing research shows that, while men are more likely than women to be promoted at the different stages of the academic career, no such difference is found when it comes to faculty retention rates. Current research on faculty retention, however, focuses on careers in science, technology, engineering, and mathematics (STEM. We extend this line of inquiry to the social sciences.We follow 2,218 tenure-track assistant professors hired since 1990 in seven social science disciplines at nineteen U.S. universities from time of hire to time of departure. We also track their time to promotion to associate and full professor. Using survival analysis, we examine gender differences in time to departure and time to promotion. Our methods account for censoring and unobserved heterogeneity, as well as effect heterogeneity across disciplines and cohorts.We find no statistically significant differences between genders in faculty retention. However, we do find that men are more likely to be granted tenure than women. When it comes to promotion to full professor, the results are less conclusive, as the effect of gender is sensitive to model specification.The results corroborate previous findings about gender patterns in faculty retention and promotion. They suggest that advances have been made when it comes to gender equality in retention and promotion, but important differences still persist.

  16. Colorectal cancer liver metastases: long-term survival and progression-free survival after thermal ablation using magnetic resonance-guided laser-induced interstitial thermotherapy in 594 patients: analysis of prognostic factors.

    Science.gov (United States)

    Vogl, Thomas J; Dommermuth, Alena; Heinle, Britta; Nour-Eldin, Nour-Eldin A; Lehnert, Thomas; Eichler, Katrin; Zangos, Stephan; Bechstein, Wolf O; Naguib, Nagy N N

    2014-01-01

    The purpose of this study was the evaluation of prognostic factors for long-term survival and progression-free survival (PFS) after treatment of colorectal cancer (CRC) liver metastases with magnetic resonance-guided laser-induced interstital thermotherapy (LITT). We included 594 patients (mean age, 61.2 years) with CRC liver metastases who were treated with LITT. The statistical analysis of the long-term survival and PFS were based on the Kaplan-Meier method. The Cox regression model tested different parameters that could be of prognostic value. The tested prognostic factors were the following: sex, age, the location of primary tumor, the number of metastases, the maximal diameter and total volume of metastases and necroses, the quotient of total volumes of metastases and necroses, the time of appearance of liver metastases and location in the liver, the TNM classification of CRC, extrahepatic metastases, and neoadjuvant treatments. The median survival was 25 months starting from the date of the first LITT. The 1-, 2-, 3-, 4-, and 5-year survival rates were 78%, 50.1%, 28%, 16.4%, and 7.8%, respectively. The median PFS was 13 months. The 1-, 2-, 3-, 4-, and 5-year PFS rates were 51.3%, 35.4%, 30.7%, 25.4%, and 22.3%, respectively. The number of metastases and their maximal diameter were the most important prognostic factors for both long-term survival and PFS. Long-term survival was also highly influenced by the initial involvement of the lymph nodes. For patients treated with LITT for CRC liver metastases, the number and size of metastases, together with the initial lymph node status, are significant prognostic factors for long-term survival.

  17. A Twin Protection Effect? Explaining Twin Survival Advantages with a Two-Process Mortality Model.

    Directory of Open Access Journals (Sweden)

    David J Sharrow

    Full Text Available Twin studies that focus on the correlation in age-at-death between twin pairs have yielded important insights into the heritability and role of genetic factors in determining lifespan, but less attention is paid to the biological and social role of zygosity itself in determining survival across the entire life course. Using data from the Danish Twin Registry and the Human Mortality Database, we show that monozygotic twins have greater cumulative survival proportions at nearly every age compared to dizygotic twins and the Danish general population. We examine this survival advantage by fitting these data with a two-process mortality model that partitions survivorship patterns into extrinsic and intrinsic mortality processes roughly corresponding to acute, environmental and chronic, biological origins. We find intrinsic processes confer a survival advantage at older ages for males, while at younger ages, all monozygotic twins show a health protection effect against extrinsic death akin to a marriage protection effect. While existing research suggests an increasingly important role for genetic factors at very advanced ages, we conclude that the social closeness of monozygotic twins is a plausible driver of the survival advantage at ages <65.

  18. A Twin Protection Effect? Explaining Twin Survival Advantages with a Two-Process Mortality Model.

    Science.gov (United States)

    Sharrow, David J; Anderson, James J

    2016-01-01

    Twin studies that focus on the correlation in age-at-death between twin pairs have yielded important insights into the heritability and role of genetic factors in determining lifespan, but less attention is paid to the biological and social role of zygosity itself in determining survival across the entire life course. Using data from the Danish Twin Registry and the Human Mortality Database, we show that monozygotic twins have greater cumulative survival proportions at nearly every age compared to dizygotic twins and the Danish general population. We examine this survival advantage by fitting these data with a two-process mortality model that partitions survivorship patterns into extrinsic and intrinsic mortality processes roughly corresponding to acute, environmental and chronic, biological origins. We find intrinsic processes confer a survival advantage at older ages for males, while at younger ages, all monozygotic twins show a health protection effect against extrinsic death akin to a marriage protection effect. While existing research suggests an increasingly important role for genetic factors at very advanced ages, we conclude that the social closeness of monozygotic twins is a plausible driver of the survival advantage at ages <65.

  19. Tobit regression for modeling mean survival time using data subject to multiple sources of censoring.

    Science.gov (United States)

    Gong, Qi; Schaubel, Douglas E

    2018-01-22

    Mean survival time is often of inherent interest in medical and epidemiologic studies. In the presence of censoring and when covariate effects are of interest, Cox regression is the strong default, but mostly due to convenience and familiarity. When survival times are uncensored, covariate effects can be estimated as differences in mean survival through linear regression. Tobit regression can validly be performed through maximum likelihood when the censoring times are fixed (ie, known for each subject, even in cases where the outcome is observed). However, Tobit regression is generally inapplicable when the response is subject to random right censoring. We propose Tobit regression methods based on weighted maximum likelihood which are applicable to survival times subject to both fixed and random censoring times. Under the proposed approach, known right censoring is handled naturally through the Tobit model, with inverse probability of censoring weighting used to overcome random censoring. Essentially, the re-weighting data are intended to represent those that would have been observed in the absence of random censoring. We develop methods for estimating the Tobit regression parameter, then the population mean survival time. A closed form large-sample variance estimator is proposed for the regression parameter estimator, with a semiparametric bootstrap standard error estimator derived for the population mean. The proposed methods are easily implementable using standard software. Finite-sample properties are assessed through simulation. The methods are applied to a large cohort of patients wait-listed for kidney transplantation. Copyright © 2018 John Wiley & Sons, Ltd.

  20. Gene expression meta-analysis identifies chromosomal regions involved in ovarian cancer survival

    DEFF Research Database (Denmark)

    Thomassen, Mads; Jochumsen, Kirsten M; Mogensen, Ole

    2009-01-01

    Ovarian cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth, whereas others are causal for metastasis and recurrence. By using publicly available data sets, we have investigated...... the relation of gene expression and chromosomal position to identify chromosomal regions of importance for early recurrence of ovarian cancer. By use of *Gene Set Enrichment Analysis*, we have ranked chromosomal regions according to their association to survival. Over-representation analysis including 1...... summarized mutation load in these regions by a combined mutation score that is statistical significantly associated to survival by analysis in the data sets used for identification of the regions. Furthermore, the prognostic value of the combined mutation score was validated in an independent large data set...

  1. Desiccation survival in an Antarctic nematode: molecular analysis using expressed sequenced tags

    Directory of Open Access Journals (Sweden)

    Wall Diana H

    2009-02-01

    Full Text Available Abstract Background Nematodes are the dominant soil animals in Antarctic Dry Valleys and are capable of surviving desiccation and freezing in an anhydrobiotic state. Genes induced by desiccation stress have been successfully enumerated in nematodes; however we have little knowledge of gene regulation by Antarctic nematodes which can survive multiple environmental stresses. To address this problem we investigated the genetic responses of a nematode species, Plectus murrayi, that is capable of tolerating Antarctic environmental extremes, in particular desiccation and freezing. In this study, we provide the first insight into the desiccation induced transcriptome of an Antarctic nematode through cDNA library construction and suppressive subtractive hybridization. Results We obtained 2,486 expressed sequence tags (ESTs from 2,586 clones derived from the cDNA library of desiccated P. murrayi. The 2,486 ESTs formed 1,387 putative unique transcripts of which 523 (38% had matches in the model-nematode Caenorhabditis elegans, 107 (7% in nematodes other than C. elegans, 153 (11% in non-nematode organisms and 605 (44% had no significant match to any sequences in the current databases. The 1,387 unique transcripts were functionally classified by using Gene Ontology (GO hierarchy and the Kyoto Encyclopedia of Genes and Genomes (KEGG database. The results indicate that the transcriptome contains a group of transcripts from diverse functional areas. The subtractive library of desiccated nematodes showed 80 transcripts differentially expressed during desiccation stress, of which 28% were metabolism related, 19% were involved in environmental information processing, 28% involved in genetic information processing and 21% were novel transcripts. Expression profiling of 14 selected genes by quantitative Real-time PCR showed 9 genes significantly up-regulated, 3 down-regulated and 2 continuously expressed in response to desiccation. Conclusion The establishment of a

  2. Desiccation survival in an Antarctic nematode: molecular analysis using expressed sequenced tags.

    Science.gov (United States)

    Adhikari, Bishwo N; Wall, Diana H; Adams, Byron J

    2009-02-09

    Nematodes are the dominant soil animals in Antarctic Dry Valleys and are capable of surviving desiccation and freezing in an anhydrobiotic state. Genes induced by desiccation stress have been successfully enumerated in nematodes; however we have little knowledge of gene regulation by Antarctic nematodes which can survive multiple environmental stresses. To address this problem we investigated the genetic responses of a nematode species, Plectus murrayi, that is capable of tolerating Antarctic environmental extremes, in particular desiccation and freezing. In this study, we provide the first insight into the desiccation induced transcriptome of an Antarctic nematode through cDNA library construction and suppressive subtractive hybridization. We obtained 2,486 expressed sequence tags (ESTs) from 2,586 clones derived from the cDNA library of desiccated P. murrayi. The 2,486 ESTs formed 1,387 putative unique transcripts of which 523 (38%) had matches in the model-nematode Caenorhabditis elegans, 107 (7%) in nematodes other than C. elegans, 153 (11%) in non-nematode organisms and 605 (44%) had no significant match to any sequences in the current databases. The 1,387 unique transcripts were functionally classified by using Gene Ontology (GO) hierarchy and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The results indicate that the transcriptome contains a group of transcripts from diverse functional areas. The subtractive library of desiccated nematodes showed 80 transcripts differentially expressed during desiccation stress, of which 28% were metabolism related, 19% were involved in environmental information processing, 28% involved in genetic information processing and 21% were novel transcripts. Expression profiling of 14 selected genes by quantitative Real-time PCR showed 9 genes significantly up-regulated, 3 down-regulated and 2 continuously expressed in response to desiccation. The establishment of a desiccation EST collection for Plectus murrayi, a

  3. Decompression Sickness After Air Break in Prebreathe Described with a Survival Model

    Science.gov (United States)

    Conkin, J.; Pilmanis, A. A.

    2010-01-01

    Data from Brooks City-Base show the decompression sickness (DCS) and venous gas emboli (VGE) consequences of air breaks in a resting 100% O2 prebreathe (PB) prior to a hypobaric exposure. METHODS: DCS and VGE survival times from 95 controls for a 60 min PB prior to 2-hr or 4-hr exposures to 4.37 psia are statistically compared to 3 break in PB conditions: a 10 min (n=40), 20 min (n=40), or 60 min break (n=32) 30 min into the PB followed by 30 min of PB. Ascent rate was 1,524 meters / min and all exposures included light exercise and 4 min of VGE monitoring of heart chambers at 16 min intervals. DCS survival time for combined control and air breaks were described with an accelerated log logistic model where exponential N2 washin during air break was described with a 10 min half-time and washout during PB with a 60 min half-time. RESULTS: There was no difference in VGE or DCS survival times among 3 different air breaks, or when air breaks were compared to control VGE times. However, 10, 20, and 60 min air breaks had significantly earlier survival times compared to control DCS times, certainly early in the exposures. CONCLUSION: Air breaks of 10, 20, and 60 min after 30 min of a 60 min PB reduced DCS survival time. The survival model combined discrete comparisons into a global description mechanistically linked to asymmetrical N2 washin and washout kinetics based on inspired pN2. Our unvalidated regression is used to compute additional PB time needed to compensate for an air break in PB within the range of tested conditions.

  4. Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model.

    Science.gov (United States)

    Audureau, Etienne; Chivet, Anaïs; Ursu, Renata; Corns, Robert; Metellus, Philippe; Noel, Georges; Zouaoui, Sonia; Guyotat, Jacques; Le Reste, Pierre-Jean; Faillot, Thierry; Litre, Fabien; Desse, Nicolas; Petit, Antoine; Emery, Evelyne; Lechapt-Zalcman, Emmanuelle; Peltier, Johann; Duntze, Julien; Dezamis, Edouard; Voirin, Jimmy; Menei, Philippe; Caire, François; Dam Hieu, Phong; Barat, Jean-Luc; Langlois, Olivier; Vignes, Jean-Rodolphe; Fabbro-Peray, Pascale; Riondel, Adeline; Sorbets, Elodie; Zanello, Marc; Roux, Alexandre; Carpentier, Antoine; Bauchet, Luc; Pallud, Johan

    2017-11-20

    We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.

  5. Survival analysis of the association between antenatal care attendance and neonatal mortality in 57 low- and middle-income countries.

    Science.gov (United States)

    Doku, David T; Neupane, Subas

    2017-10-01

    Neonatal mortality is unacceptably high in most low- and middle-income countries (LMICs). In these countries, where access to emergency obstetric services is limited, antenatal care (ANC) utilization offers improved maternal health and birth outcomes. However, evidence for this is scanty and mixed. We explored the association between attendance for ANC and survival of neonates in 57 LMICs. Employing standardized protocols to ensure comparison across countries, we used nationally representative cross-sectional data from 57 LMICs (N = 464 728) to investigate the association between ANC visits and neonatal mortality. Cox proportional hazards multivariable regression models and meta-regression analysis were used to analyse pooled data from the countries. Kaplan-Meier survival curves were used to describe the patterns of neonatal survival in each region. After adjusting for potential confounding factors, we found 55% lower risk of neonatal mortality [hazard ratio (HR) 0.45, 95% confidence interval (CI) 0.42-0.48] among women who met both WHO recommendations for ANC (first visit within the first trimester and at least four visits during pregnancy) in pooled analysis. Furthermore, meta-analysis of country-level risk shows 32% lower risk of neonatal mortality (HR 0.68, 95% CI 0.61-0.75) among those who met at least one WHO recommendation. In addition, ANC attendance was associated with lower neonatal mortality in all the regions except in the Middle East and North Africa. ANC attendance is protective against neonatal mortality in the LMICs studied, although differences exist across countries and regions. Increasing ANC visits, along with other known effective interventions, can improve neonatal survival in these countries.

  6. Survival analysis and prognostic factors of timing of first childbirth among women in Nigeria.

    Science.gov (United States)

    Fagbamigbe, Adeniyi Francis; Idemudia, Erhabor Sunday

    2016-05-13

    First childbirth in a woman's life is one of the most important events in her life. It marks a turnaround when she might have to drop roles of career building and education, for motherhood and parenthood. The timing of the commencement of these roles affects the child bearing behavior of women as they progress in their reproductive ages. Prevalent early first childbirth in Nigeria has been reported as the main cause of high population growth and high  fertility, mortality and morbidity among women, but little has been documented on the progression into first birth as well as factors affecting it in Nigeria. This paper modelled timing of first birth among women in Nigeria and determined socio-demographic and other factors affecting its timing. We hypothesized that background characteristics of a woman will influence her progression into having first birth. We developed and fitted a survival analysis model to understand the timing of first birth among women in Nigeria using a national representative 2013 NDHS data. Women with no children were right censored as of the date of the survey. The Kaplan Meier survival function was used to estimate the probabilities of first birth not occurring until certain ages of women while Cox proportional hazard regression was used to model the timing of first births at 5 % significance level. About 75.7 % of the respondents had given birth in the Northern region of Nigerian compared with 63.8 % in the South. Half (50.1 %) of the first childbirth occurred within the 15-19 years age bracket and 38.1 % within 20-29 years. The overall median survival time to first birth was 20 years (North 19, South 22), 27 years among women with higher education and 18 years for those with no formal education. The adjusted hazard of first birth was higher in the Northern region of Nigeria than in the South (aHR = 1.24, 95 % CI: 1.20-1.27), and higher in rural areas than in urban areas (aHR = 1.15, 95 % CI: 1.12-1.19). Also, hazard of earlier first

  7. Renal cell carcinoma in end-stage renal disease: Multi-institutional comparative analysis of survival.

    Science.gov (United States)

    Song, Cheryn; Hong, Sung Hoo; Chung, Jin Soo; Byun, Seok Soo; Kwak, Cheol; Jeong, Chang Wook; Seo, Seong Il; Jeon, Hwang Gyun; Seo, Ill Young

    2016-06-01

    To describe the clinical features of renal cell carcinoma arising in end-stage renal disease and to compare survival outcomes after definitive treatment with non-end-stage renal disease renal cell carcinoma. Data of 181 consecutive patients with end-stage renal disease renal cell carcinoma who had received surgical treatment between 1995 and 2011 at seven institutions were reviewed. Data of 362 non-end-stage renal disease renal cell carcinoma patients matched for clinicopathological parameters who received surgery at Asan Medical Center during the same study period were also reviewed. The two study groups were compared with respect to recurrence-free, cancer-specific, and overall survival by Kaplan-Meier analysis and Cox proportional hazards method. Mean follow up was 40 ± 34.2 months after surgery. Median tumor size was 2.5 cm (interquartile range 1.5-4.5), and pathological tumor stage was T1 in 78%, T2 in 7.1% and T3 and higher in 14.9%. Tumor histological type was clear cell in 63%, papillary in 17%, chromophobe in 5%, clear cell papillary in 2.8% and acquired cystic disease-related in 6.1%. Compared with the controls, the stage-specific 5-year recurrence-free survival was similar (87.6 vs 88.5%), but cancer-specific and overall survival was significantly lower. On multivariate analysis, end-stage renal disease renal cell carcinoma was not a predictor for recurrence-free survival, but a significant predictor for cancer-specific (hazard ratio 4.07, 95% confidence interval 2.08-7.94) and overall survival (hazard ratio 3.13, 95% confidence interval 1.66-5.96). End-stage renal disease renal cell carcinoma seems to have comparable stage-specific recurrence-free, but poorer cancer-specific and overall survival compared with non-end-stage renal disease renal cell carcinoma. As patients with end-stage renal disease are a high-risk population for renal cell carcinoma, routine radiographic screening to improve survival outcomes should be further investigated. © 2016

  8. Influence of Androgen Receptor Expression on the Survival Outcomes in Breast Cancer: A Meta-Analysis.

    Science.gov (United States)

    Kim, Yoonseok; Jae, Eunae; Yoon, Myunghee

    2015-06-01

    Despite the fact that the androgen receptor (AR) is known to be involved in the pathogenesis of breast cancer, its prognostic effect remains controversial. In this meta-analysis, we explored AR expression and its impact on survival outcomes in breast cancer. We searched PubMed, EMBASE, Cochrane Library, ScienceDirect, SpringerLink, and Ovid databases and references of articles to identify studies reporting data until December 2013. Disease-free survival (DFS) and overall survival (OS) were analyzed by extracting the number of patients with recurrence and survival according to AR expression. There were 16 articles that met the criteria for inclusion in our meta-analysis. DFS and OS were significantly longer in patients with AR expression compared with patients without AR expression (odds ratio [OR], 0.60; 95% confidence interval [CI], 0.40-0.90; OR, 0.53; 95% CI, 0.38-0.73, respectively). In addition, hormone receptor (HR) positive patients had a longer DFS when AR was also expressed (OR, 0.63; 95% CI, 0.41-0.98). For patients with triple negative breast cancer (TNBC), AR expression was also associated with longer DFS and OS (OR, 0.44, 95% CI, 0.26-0.75; OR, 0.26, 95% CI, 0.12-0.55, respectively). Furthermore, AR expression was associated with a longer DFS and OS in women (OR, 0.42, 95% CI, 0.27-0.64; OR, 0.47, 95% CI, 0.38-0.59, respectively). However, in men, AR expression was associated with a worse DFS (OR, 6.00; 95% CI, 1.46-24.73). Expression of AR in breast cancer might be associated with better survival outcomes, especially in patients with HR-positive tumors and TNBC, and women. Based on this meta-analysis, we propose that AR expression might be related to prognostic features and contribute to clinical outcomes.

  9. Model evaluation based on the negative predictive value for interval-censored survival outcomes.

    Science.gov (United States)

    Han, Seungbong; Tsui, Kam-Wah; Andrei, Adin-Cristian

    2017-04-01

    In many cohort studies, time to events such as disease recurrence is recorded in an interval-censored format. An important objective is to predict patient outcomes. Clinicians are interested in predictive covariates. Prediction rules based on the receiver operating characteristic curve alone are not related to the survival endpoint. We propose a model evaluation strategy to leverage the predictive accuracy based on negative predictive functions. Our proposed method makes very few assumptions and only requires a working model to obtain the regression coefficients. A nonparametric estimate of the predictive accuracy provides a simple and flexible approach for model evaluation to interval-censored survival outcomes. The implementation effort is minimal, therefore this method has an increased potential for immediate use in biomedical data analyses. Simulation studies and a breast cancer trial example further illustrate the practical advantages of this approach.

  10. Jointly modeling the relationship between longitudinal and survival data subject to left truncation with applications to cystic fibrosis.

    Science.gov (United States)

    Piccorelli, Annalisa V; Schluchter, Mark D

    2012-12-20

    Numerous methods for joint analysis of longitudinal measures of a continuous outcome y and a time to event outcome T have recently been developed either to focus on the longitudinal data y while correcting for nonignorable dropout, to predict the survival outcome T using the longitudinal data y, or to examine the relationship between y and T. The motivating problem for our work is in joint modeling of the serial measurements of pulmonary function (FEV1% predicted) and survival in cystic fibrosis (CF) patients using registry data. Within the CF registry data, an additional complexity is that not all patients have been followed from birth; therefore, some patients have delayed entry into the study while others may have been missed completely, giving rise to a left truncated distribution. This paper shows in joint modeling situations where y and T are not independent, that it is necessary to account for this left truncation to obtain valid parameter estimates related to both survival and the longitudinal marker. We assume a linear random effects model for FEV1% predicted, where the random intercept and slope of FEV1% predicted, along with a specified transformation of the age at death follow a trivariate normal distribution. We develop an expectation-maximization algorithm for maximum likelihood estimation of parameters, which takes left truncation and right censoring of survival times into account. The methods are illustrated using simulation studies and using data from CF patients in a registry followed at Rainbow Babies and Children's Hospital, Cleveland, OH. Copyright © 2012 John Wiley & Sons, Ltd.

  11. [An analysis of cancer survival narratives using computerized text analysis program].

    Science.gov (United States)

    Kim, Dal Sook; Park, Ah Hyun; Kang, Nam Jun

    2014-06-01

    This study was done to explore experiences of persons living through the periods of cancer diagnosis, treatment, and self-care. With permission, texts of 29 cancer survival narratives (8 men and 21 women, winners in contests sponsored by two institutes), were analyzed using Kang's Korean-Computerized-Text-Analysis-Program where the commonly used Korean-Morphological-Analyzer and the 21st-century-Sejong-Modern-Korean-Corpora representing laymen's Korean-language-use are connected. Experiences were explored based on words included in 100 highly-used-morphemes. For interpretation, we used 'categorizing words by meaning', 'comparing use-rate by periods and to the 21st-century-Sejong-Modern-Korean-Corpora', and highly-used-morphemes that appeared only in a specific period. The most highly-used-word-morpheme was first-person-pronouns followed by, diagnosis·treatment-related-words, mind-expression-words, cancer, persons-in-meaningful-interaction, living and eating, information-related-verbs, emotion-expression-words, with 240 to 0.8 times for layman use-rate. 'Diagnosis-process', 'cancer-thought', 'things-to-come-after-diagnosis', 'physician·husband', 'result-related-information', 'meaningful-things before diagnosis-period', and 'locus-of-cause' dominated the life of the diagnosis-period. 'Treatment', 'unreliable-body', 'husband · people · mother · physician', 'treatment-related-uncertainty', 'hard-time', and 'waiting-time represented experiences in the treatment-period. Themes of living in the self-care-period were complex and included 'living-as-a-human', 'self-managing-of-diseased-body', 'positive-emotion', and 'connecting past · present · future'. The results show that the experience of living for persons with cancer is influenced by each period's own situational-characteristics. Experiences of the diagnosis and treatment-period are negative disease-oriented while that of the self-care period is positive present-oriented.

  12. Simple models based on gamma-glutamyl transpeptidase and platelets for predicting survival in hepatitis B-associated hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    Pang Q

    2016-04-01

    Full Text Available Qing Pang, Jian-Bin Bi, Zhi-Xin Wang, Xin-Sen Xu, Kai Qu, Run-Chen Miao, Wei Chen, Yan-Yan Zhou, Chang Liu Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Shaanxi Province, People’s Republic of China Background: Several hepatic cirrhosis-derived noninvasive models have been developed to predict the incidence and outcomes of hepatocellular carcinoma (HCC. We aimed to investigate the prognostic significance of the two novel established cirrhosis-associated models based on gamma-glutamyl transpeptidase (GGT and platelets in hepatitis B-associated HCC. Methods: We retrospectively evaluated 182 HCC patients with positive hepatitis B surface antigen who received radical therapy at a single institution between 2002 and 2012. Laboratory data prior to operation were collected to calculate the GGT to platelets ratio (GPR and the S-index. Predictive factors associated with overall survival and recurrence-free survival were assessed using log-rank test and multivariate Cox analysis. Additional analyses were performed after patients were stratified based on cirrhosis status, tumor size, therapy methods, and so forth, to investigate the prognostic significance in different subgroups. Results: During a median follow-up time of 45.0 months, a total of 88 (48.4% patients died and 79 (43.4% patients recurred. The cut-off points for GPR and S-index in predicting death were determined to be 0.76 and 0.56, respectively. Compared with patients with a lower GPR, those with GPR ≥0.76 had a higher probability of cirrhosis and a larger tumor (both P<0.05. GPR and S-index were both found to be significantly associated with survival by univariate log-rank test. Multivariate analysis identified tumor size ≥5 and high level of GPR, but not high Barcelona Clinic Liver Cancer stage or S-index, as independent factors for predicting poor overall survival and recurrence-free survival. Conclusion: The GPR is

  13. SDI CFD MODELING ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S.

    2011-05-05

    The Savannah River Remediation (SRR) Organization requested that Savannah River National Laboratory (SRNL) develop a Computational Fluid Dynamics (CFD) method to mix and blend the miscible contents of the blend tanks to ensure the contents are properly blended before they are transferred from the blend tank; such as, Tank 50H, to the Salt Waste Processing Facility (SWPF) feed tank. The work described here consists of two modeling areas. They are the mixing modeling analysis during miscible liquid blending operation, and the flow pattern analysis during transfer operation of the blended liquid. The transient CFD governing equations consisting of three momentum equations, one mass balance, two turbulence transport equations for kinetic energy and dissipation rate, and one species transport were solved by an iterative technique until the species concentrations of tank fluid were in equilibrium. The steady-state flow solutions for the entire tank fluid were used for flow pattern analysis, for velocity scaling analysis, and the initial conditions for transient blending calculations. A series of the modeling calculations were performed to estimate the blending times for various jet flow conditions, and to investigate the impact of the cooling coils on the blending time of the tank contents. The modeling results were benchmarked against the pilot scale test results. All of the flow and mixing models were performed with the nozzles installed at the mid-elevation, and parallel to the tank wall. From the CFD modeling calculations, the main results are summarized as follows: (1) The benchmark analyses for the CFD flow velocity and blending models demonstrate their consistency with Engineering Development Laboratory (EDL) and literature test results in terms of local velocity measurements and experimental observations. Thus, an application of the established criterion to SRS full scale tank will provide a better, physically-based estimate of the required mixing time, and

  14. Survival analysis of female dogs with mammary tumors after mastectomy: epidemiological, clinical and morphological aspects

    Directory of Open Access Journals (Sweden)

    Maria Luíza de M. Dias

    2016-03-01

    Full Text Available Abstract: Mammary gland tumors are the most common type of tumors in bitches but research on survival time after diagnosis is scarce. The purpose of this study was to investigate the relationship between survival time after mastectomy and a number of clinical and morphological variables. Data was collected retrospectively on bitches with mammary tumors seen at the Small Animal Surgery Clinic Service at the University of Brasília. All subjects had undergone mastectomy. Survival analysis was conducted using Cox's proportional hazard method. Of the 139 subjects analyzed, 68 died and 71 survived until the end of the study (64 months. Mean age was 11.76 years (SD=2.71, 53.84% were small dogs. 76.92% of the tumors were malignant, and 65.73% had both thoracic and inguinal glands affected. Survival time in months was associated with age (hazard rate ratios [HRR] =1.23, p-value =1.4x10-4, animal size (HRR between giant and small animals =2.61, p-value =0.02, nodule size (HRR =1.09, p-value =0.03, histological type (HRR between solid carcinoma and carcinoma in a mixed tumor =2.40, p-value =0.02, time between diagnosis and surgery (TDS, with HRR =1.21, p-value =2.7x10-15, and the interaction TDS*follow-up time (HRR =0.98, p-value =1.6x10-11. The present study is one of the few on the subject matter. Several important covariates were evaluated and age, animal size, nodule size, histological type, TDS and TDS*follow up time were identified as significantly associated to survival time.

  15. Nonlinear group survival in Kimura's model for the evolution of altruism.

    Science.gov (United States)

    Fontanari, José F; Serva, Maurizio

    2014-03-01

    Establishing the conditions that guarantee the spreading or the sustenance of altruistic traits in a population is the main goal of intergroup selection models. Of particular interest is the balance of the parameters associated to group size, migration and group survival against the selective advantage of the non-altruistic individuals. Here we use Kimura's diffusion model of intergroup selection to determine those conditions in the case the group survival rate is a nonlinear non-decreasing function of the proportion of altruists in a group. In the case this function is linear, there are two possible steady states which correspond to the non-altruistic and the altruistic phases. At the discontinuous transition line separating these phases there is a non-ergodic coexistence phase. For a continuous concave survival function, we find an ergodic coexistence phase that occupies a finite region of the parameter space in between the altruistic and the non-altruistic phases, and is separated from these phases by continuous transition lines. For a convex survival function, the coexistence phase disappears altogether but a bistable phase appears for which the choice of the initial condition determines whether the evolutionary dynamics leads to the altruistic or the non-altruistic steady state. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Habitat-specific breeder survival of Florida Scrub-Jays: Inferences from multistate models

    Science.gov (United States)

    Breininger, D.R.; Nichols, J.D.; Carter, G.M.; Oddy, D.M.

    2009-01-01

    Quantifying habitat-specific survival and changes in habitat quality within disturbance-prone habitats is critical for understanding population dynamics and variation in fitness, and for managing degraded ecosystems. We used 18 years of color-banding data and multistate capture-recapture models to test whether habitat quality within territories influences survival and detection probability of breeding Florida Scrub-Jays (Aphelocoma coerulescens) and to estimate bird transition probabilities from one territory quality state to another. Our study sites were along central Florida's Atlantic coast and included two of the four largest metapopulations within the species range. We developed Markov models for habitat transitions and compared these to bird transition probabilities. Florida Scrub-Jay detection probabilities ranged from 0.88 in the tall territory state to 0.99 in the optimal state; detection probabilities were intermediate in the short state. Transition probabilities were similar for birds and habitat in grid cells mapped independently of birds. Thus, bird transitions resulted primarily from habitat transitions between states over time and not from bird movement. Survival ranged from 0.71 in the short state to 0.82 in the optimal state, with tall states being intermediate. We conclude that average Florida Scrub-Jay survival will remain at levels that lead to continued population declines because most current habitat quality is only marginally suitable across most of the species range. Improvements in habitat are likely to be slow and difficult because tall states are resistant to change and the optimal state represents an intermediate transitional stage. The multistate modeling approach to quantifying survival and habitat transition probabilities is useful for quantifying habitat transition probabilities and comparing them to bird transition probabilities to test for habitat selection in dynamic environments. ?? 2009 by the Ecological society ot America.

  17. Survival analysis of patients with interval cancer undergoing gastric cancer screening by endoscopy.

    Science.gov (United States)

    Hamashima, Chisato; Shabana, Michiko; Okamoto, Mikizo; Osaki, Yoneatsu; Kishimoto, Takuji

    2015-01-01

    Interval cancer is a key factor that influences the effectiveness of a cancer screening program. To evaluate the impact of interval cancer on the effectiveness of endoscopic screening, the survival rates of patients with interval cancer were analyzed. We performed gastric cancer-specific and all-causes survival analyses of patients with screen-detected cancer and patients with interval cancer in the endoscopic screening group and radiographic screening group using the Kaplan-Meier method. Since the screening interval was 1 year, interval cancer was defined as gastric cancer detected within 1 year after a negative result. A Cox proportional hazards model was used to investigate the risk factors associated with gastric cancer-specific and all-causes death. A total of 1,493 gastric cancer patients (endoscopic screening group: n = 347; radiographic screening group: n = 166; outpatient group: n = 980) were identified from the Tottori Cancer Registry from 2001 to 2008. The gastric cancer-specific survival rates were higher in the endoscopic screening group than in the radiographic screening group and the outpatients group. In the endoscopic screening group, the gastric cancer-specific survival rate of the patients with screen-detected cancer and the patients with interval cancer were nearly equal (P = 0.869). In the radiographic screening group, the gastric cancer-specific survival rate of the patients with screen-detected cancer was higher than that of the patients with interval cancer (P = 0.009). For gastric cancer-specific death, the hazard ratio of interval cancer in the endoscopic screening group was 0.216 for gastric cancer death (95%CI: 0.054-0.868) compared with the outpatient group. The survival rate and the risk of gastric cancer death among the patients with screen-detected cancer and patients with interval cancer were not significantly different in the annual endoscopic screening. These results suggest the potential of endoscopic screening in reducing

  18. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

    Science.gov (United States)

    Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

    2013-01-01

    There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer

  19. A survival analysis using physique-adjusted tumor size of non-small cell lung cancer.

    Science.gov (United States)

    Ozeki, Naoki; Fukui, Takayuki; Kawaguchi, Koji; Nakamura, Shota; Hakiri, Shuhei; Kato, Taketo; Hirakawa, Akihiro; Yokoi, Kohei

    2017-11-29

    Differences in individual body sizes have not been well considered when analyzing the survival of patients with non-small cell lung cancer (NSCLC). We hypothesized that physique-adjusted tumor size is superior to actual tumor size in predicting the prognosis. Eight hundred and forty-two patients who underwent R0 resection of NSCLC between 2005 and 2012 were retrospectively reviewed, and overall survival (OS) was evaluated. The physique-adjusted tumor size was defined as: x-adjusted tumor size = tumor size × mean value of x/individual value of x [x = height, weight, body surface area (BSA), or body mass index (BMI)]. Tumor size category was defined as ≤2, 2-3, 3-5, 5-7, and >7 cm. The separation index (SEP), which is the weighted mean of the absolute value of estimated regression coefficients over the subgroups with respect to a reference group, was used to measure the separation of subgroups. The mean values of height, weight, BSA, and BMI were 160.7 cm, 57.6 kg, 1.59 m2, and 22.2 kg/m2, respectively. The 5-year survival rates ranged from 88-59% in the non-adjusted tumor size model (SEP 1.937), from 90-57% in the height-adjusted model (SEP 2.236), from 91-52% in the weight-adjusted model (SEP 2.146), from 90-56% in the BSA-adjusted model (SEP 2.077), and from 91-51% in the BMI-adjusted model (SEP 2.169). The physique-adjusted tumor size can separate the survival better than the actual tumor size.

  20. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe

  1. Chemoembolization With Doxorubicin-Eluting Beads for Unresectable Hepatocellular Carcinoma: Five-Year Survival Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Malagari, Katerina, E-mail: kmalag@otonet.gr [University of Athens, Second Department of Radiology (Greece); Pomoni, Mary [University of Athens, Imaging and Research Unit (Greece); Moschouris, Hippocrates, E-mail: hipmosch@gmail.com [Tzanion Hospital, Department of Radiology (Greece); Bouma, Evanthia [University of Athens, Imaging and Research Unit (Greece); Koskinas, John [Ippokration Hospital, University of Athens, Department of Internal Medicine and Hepatology (Greece); Stefaniotou, Aspasia [University of Athens, Imaging and Research Unit (Greece); Marinis, Athanasios [Tzanion Hospital, Department of Surgery (Greece); Kelekis, Alexios; Alexopoulou, Efthymia [University of Athens, Second Department of Radiology (Greece); Chatziioannou, Achilles [University of Athens, First Department of Radiology (Greece); Chatzimichael, Katerina [University of Athens, Second Department of Radiology (Greece); Dourakis, Spyridon [Ippokration Hospital, University of Athens, Department of Internal Medicine and Hepatology (Greece); Kelekis, Nikolaos [University of Athens, Second Department of Radiology (Greece); Rizos, Spyros [Tzanion Hospital, Department of Surgery (Greece); Kelekis, Dimitrios [University of Athens, Imaging and Research Unit (Greece)

    2012-10-15

    Purpose: The purpose of this study was to report on the 5-year survival of hepatocellular carcinoma (HCC) patients treated with DC Bead loaded with doxorubicin (DEB-DOX) in a scheduled scheme in up to three treatments and thereafter on demand. Materials and Methods: 173 HCC patients not suitable for curable treatments were prospectively enrolled (mean age 70.4 {+-} 7.4 years). Child-Pugh (Child) class was A/B (102/71 [59/41 %]), Okuda stage was 0/1/2 (91/61/19 [53.2/35.7/11.1 %]), and mean lesion diameter was 7.6 {+-} 2.1 cm. Lesion morphology was one dominant {<=}5 cm (22 %), one dominant >5 cm (41.6 %), multifocal {<=}5 (26 %), and multifocal >5 (10.4 %). Results: Overall survival at 1, 2, 3, 4, and 5 years was 93.6, 83.8, 62, 41.04, and 22.5 %, with higher rates achieved in Child class A compared with Child class B patients (95, 88.2, 61.7, 45, and 29.4 % vs. 91.5, 75, 50.7, 35.2, and 12.8 %). Mean overall survival was 43.8 months (range 1.2-64.8). Cumulative survival was better for Child class A compared with Child class B patients (p = 0.029). For patients with dominant lesions {<=}5 cm 1-, 2-, 3-, 4-, and 5-year survival rates were 100, 95.2, 71.4, 66.6, and 47.6 % for Child class A and 94.1, 88.2, 58.8, 41.2, 29.4, and 23.5 % for Child class B patients. Regarding DEB-DOX treatment, multivariate analysis identified number of lesions (p = 0.033), lesion vascularity (p < 0.0001), initially achieved complete response (p < 0.0001), and objective response (p = 0.046) as significant and independent determinants of 5-year survival. Conclusion: DEB-DOX results, with high rates of 5-year survival for patients, not amenable to curative treatments. Number of lesions, lesion vascularity, and local response were significant independent determinants of 5-year survival.

  2. Survival Analysis of Advanced HCC Treated with Radioembolization: Comparing Impact of Clinical Performance Status Versus Vascular Invasion/Metastases.

    Science.gov (United States)

    Ali, Rehan; Gabr, Ahmed; Abouchaleh, Nadine; Al Asadi, Ali; Mora, Ronald A; Kulik, Laura; Abecassis, Michael; Riaz, Ahsun; Salem, Riad; Lewandowski, Robert J

    2017-09-06

    In this study, we aim to compare the effects of prognostic indicators on survival analysis for Barcelona Clinic Liver Cancer (BCLC) C patients undergoing yttrium-90 radioembolization (Y-90). A prospectively acquired database (2003-2017) for BCLC C hepatocellular carcinoma (HCC) patients that underwent radioembolization with Y-90 was searched. The criteria for BCLC C status (Eastern Cooperative Oncology Group (ECOG) performance status (PS) of 1 or 2, metastases, and/or portal vein thrombosis (PVT)) were recorded. Kaplan-Meier survival analyses were performed from the date of the first radioembolization with Y-90, censored to curative treatment, to determine median overall survival (OS). Cox regression hazards model was used for multivariate analyses. Significance was set at P < 0.05. 547 BCLC C patients treated with radioembolization with Y-90 had a median OS of 10.7 months (range: 9.5-12.9). 43% (233 of 547) patients classified as BCLC C solely by their ECOG PS had a median OS of 19.4 months (14.7-23.7); 57% (314 of 547) patients with PVT/metastases had a median OS of 7.7 months (6.7-8.7). On multivariate analysis, ECOG PS was not found to be a statistically significant prognostic indicator of OS in BCLC C whereas metastases and PVT exhibited hazards ratios (95%CI) of 0.51 (0.38-0.69) and 0.49 (0.38-0.63), respectively (P < 0.0001). Patients classified as BCLC C due to ECOG PS 1 demonstrated longer survival when compared to those presenting with PVT, metastases and/or ECOG PS 2. Hence, ECOG PS 1, as an isolated variable, may not be a true indicator of advanced disease.

  3. Cardiopulmonary Bypass has No Significant Impact on Survival in Patients Undergoing Nephrectomy and Level III-IV Inferior Vena Cava Thrombectomy: Multi-Institutional Analysis.

    Science.gov (United States)

    Nguyen, Hao G; Tilki, Derya; Dall'Era, Marc A; Durbin-Johnson, Blythe; Carballido, Joaquín A; Chandrasekar, Thenappan; Chromecki, Thomas; Ciancio, Gaetano; Daneshmand, Siamak; Gontero, Paolo; Gonzalez, Javier; Haferkamp, Axel; Hohenfellner, Markus; Huang, William C; Espinós, Estefania Linares; Mandel, Philipp; Martinez-Salamanca, Juan I; Master, Viraj A; McKiernan, James M; Montorsi, Francesco; Novara, Giacomo; Pahernik, Sascha; Palou, Juan; Pruthi, Raj S; Rodriguez-Faba, Oscar; Russo, Paul; Scherr, Douglas S; Shariat, Shahrokh F; Spahn, Martin; Terrone, Carlo; Vergho, Daniel; Wallen, Eric M; Xylinas, Evanguelos; Zigeuner, Richard; Libertino, John A; Evans, Christopher P

    2015-08-01

    The impact of cardiopulmonary bypass in level III-IV tumor thrombectomy on surgical and oncologic outcomes is unknown. We determine the impact of cardiopulmonary bypass on overall and cancer specific survival, as well as surgical complication rates and immediate outcomes in patients undergoing nephrectomy and level III-IV tumor thrombectomy with or without cardiopulmonary bypass. We retrospectively analyzed 362 patients with renal cell cancer and with level III or IV tumor thrombus from 1992 to 2012 at 22 U.S. and European centers. Cox proportional hazards models were used to compare overall and cancer specific survival between patients with and without cardiopulmonary bypass. Perioperative mortality and complication rates were assessed using logistic regression analyses. Median overall survival was 24.6 months in noncardiopulmonary bypass cases and 26.6 months in cardiopulmonary bypass cases. Overall survival and cancer specific survival did not differ significantly in both groups on univariate analysis or when adjusting for known risk factors. On multivariate analysis no significant differences were seen in hospital length of stay, Clavien 1-4 complication rate, intraoperative or 30-day mortality and cancer specific survival. Limitations include the retrospective nature of the study. In our multi-institutional analysis the use of cardiopulmonary bypass did not significantly impact cancer specific survival or overall survival in patients undergoing nephrectomy and level III or IV tumor thrombectomy. Neither approach was independently associated with increased mortality on multivariate analysis. Greater surgical complications were not independently associated with the use of cardiopulmonary bypass. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  4. Survival analysis of colorectal cancer patients with tumor recurrence using global score test methodology

    Science.gov (United States)

    Zain, Zakiyah; Aziz, Nazrina; Ahmad, Yuhaniz; Azwan, Zairul; Raduan, Farhana; Sagap, Ismail

    2014-12-01

    Colorectal cancer is the third and the second most common cancer worldwide in men and women respectively, and the second in Malaysia for both genders. Surgery, chemotherapy and radiotherapy are among the options available for treatment of patients with colorectal cancer. In clinical trials, the main purpose is often to compare efficacy between experimental and control treatments. Treatment comparisons often involve several responses or endpoints, and this situation complicates the analysis. In the case of colorectal cancer, sets of responses concerned with survival times include: times from tumor removal until the first, the second and the third tumor recurrences, and time to death. For a patient, the time to recurrence is correlated to the overall survival. In this study, global score test methodology is used in combining the univariate score statistics for comparing treatments with respect to each survival endpoint into a single statistic. The data of tumor recurrence and overall survival of colorectal cancer patients are taken from a Malaysian hospital. The results are found to be similar to those computed using the established Wei, Lin and Weissfeld method. Key factors such as ethnic, gender, age and stage at diagnose are also reported.

  5. Survival analysis of colorectal cancer patients with tumor recurrence using global score test methodology

    Energy Technology Data Exchange (ETDEWEB)

    Zain, Zakiyah, E-mail: zac@uum.edu.my; Ahmad, Yuhaniz, E-mail: yuhaniz@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, UUM Sintok 06010, Kedah (Malaysia); Azwan, Zairul, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com; Raduan, Farhana, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com; Sagap, Ismail, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com [Surgery Department, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, 56000 Bandar Tun Razak, Kuala Lumpur (Malaysia); Aziz, Nazrina, E-mail: nazrina@uum.edu.my

    2014-12-04

    Colorectal cancer is the third and the second most common cancer worldwide in men and women respectively, and the second in Malaysia for both genders. Surgery, chemotherapy and radiotherapy are among the options available for treatment of patients with colorectal cancer. In clinical trials, the main purpose is often to compare efficacy between experimental and control treatments. Treatment comparisons often involve several responses or endpoints, and this situation complicates the analysis. In the case of colorectal cancer, sets of responses concerned with survival times include: times from tumor removal until the first, the second and the third tumor recurrences, and time to death. For a patient, the time to recurrence is correlated to the overall survival. In this study, global score test methodology is used in combining the univariate score statistics for comparing treatments with respect to each survival endpoint into a single statistic. The data of tumor recurrence and overall survival of colorectal cancer patients are taken from a Malaysian hospital. The results are found to be similar to those computed using the established Wei, Lin and Weissfeld method. Key factors such as ethnic, gender, age and stage at diagnose are also reported.

  6. Viability of Iberian x Meishan F2 newborn pigs. II. Survival analysis up to weaning.

    Science.gov (United States)

    Casellas, J; Noguera, J L; Varona, L; Sánchez, A; Arqué, M; Piedrafita, J

    2004-07-01

    Iberian x Meishan F2 piglet's preweaning survivability was analyzed using categorical data regression procedures within the proportional hazards assumption. A frailty sire model was assumed with the litter effect treated as an additional random source of variation. Moreover, the relative birth weight within litter and the litter effect were considered time-dependent covariates that changed their values in the second day of life due to cross fostering carried out to standardize litters. Six variables had a significant effect on survivability: birth weight (P piglets (Piglets that were small in relation to their siblings (relative birth weight within litter) also suffered an increased death risk, with a hazard ratio of 1.81 (P Piglets with a rectal temperature lower than 35.4 degrees C 60 min after birth showed the highest hazard ratio (7.18; P piglet survival involves several systematic influences related to birth weight, thermoregulatory ability, and injuries suffered during gestation and farrowing. The genetic variance was small compared with those generated by the common environment, for which the genetic improvement of piglet survival seems difficult.

  7. Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

    Energy Technology Data Exchange (ETDEWEB)

    Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

    2011-11-15

    Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

  8. NTCP modelling of lung toxicity after SBRT comparing the universal survival curve and the linear quadratic model for fractionation correction.

    Science.gov (United States)

    Wennberg, Berit M; Baumann, Pia; Gagliardi, Giovanna; Nyman, Jan; Drugge, Ninni; Hoyer, Morten; Traberg, Anders; Nilsson, Kristina; Morhed, Elisabeth; Ekberg, Lars; Wittgren, Lena; Lund, Jo-Åsmund; Levin, Nina; Sederholm, Christer; Lewensohn, Rolf; Lax, Ingmar

    2011-05-01

    In SBRT of lung tumours no established relationship between dose-volume parameters and the incidence of lung toxicity is found. The aim of this study is to compare the LQ model and the universal survival curve (USC) to calculate biologically equivalent doses in SBRT to see if this will improve knowledge on this relationship. Toxicity data on radiation pneumonitis grade 2 or more (RP2+) from 57 patients were used, 10.5% were diagnosed with RP2+. The lung DVHs were corrected for fractionation (LQ and USC) and analysed with the Lyman- Kutcher-Burman (LKB) model. In the LQ-correction α/β = 3 Gy was used and the USC parameters used were: α/β = 3 Gy, D(0) = 1.0 Gy, [Formula: see text] = 10, α = 0.206 Gy(-1) and d(T) = 5.8 Gy. In order to understand the relative contribution of different dose levels to the calculated NTCP the concept of fractional NTCP was used. This might give an insight to the questions of whether "high doses to small volumes" or "low doses to large volumes" are most important for lung toxicity. NTCP analysis with the LKB-model using parameters m = 0.4, D(50) = 30 Gy resulted for the volume dependence parameter (n) with LQ correction n = 0.87 and with USC correction n = 0.71. Using parameters m = 0.3, D(50) = 20 Gy n = 0.93 with LQ correction and n = 0.83 with USC correction. In SBRT of lung tumours, NTCP modelling of lung toxicity comparing models (LQ,USC) for fractionation correction, shows that low dose contribute less and high dose more to the NTCP when using the USC-model. Comparing NTCP modelling of SBRT data and data from breast cancer, lung cancer and whole lung irradiation implies that the response of the lung is treatment specific. More data are however needed in order to have a more reliable modelling.

  9. Electrolyte Disturbances Are Associated with Non-Survival in Dogs—A Multivariable Analysis

    Directory of Open Access Journals (Sweden)

    Robert Goggs

    2017-08-01

    Full Text Available Electrolyte disorders have been individually associated with mortality in small populations of dogs and cats with specific conditions, but the associations and interactions between electrolyte disturbances and outcome have not been evaluated in a large, heterogeneous population. It was hypothesized that abnormalities of sodium, chloride, potassium, and calcium concentrations would be independently and proportionately associated with death from natural causes and with all-cause mortality in dogs. An electronic database containing 33,117 electrolyte profiles was constructed to retrospectively assess the association between disorders of sodium, potassium, corrected chloride, and ionized calcium concentrations with non-survival and with death excluding euthanasia by multivariable modeling. A second database containing 11,249 records was used to validate the models constructed from the first database. All four electrolytes assessed had non-linear U-shaped associations with case fatality rates, wherein concentrations clustered around the reference interval had the lowest case fatality rates, while progressively abnormal concentrations were associated with proportionately increased risk of non-survival (AUROC 0.624 or death (AUROC 0.678. Multivariable modeling suggested that these electrolyte disturbances were associated with non-survival and with death from natural causes independent of each other. This study suggests that measurement of electrolyte concentrations is an important component of the assessment of dogs in emergency rooms or intensive care units. Future studies should focus on confirming these associations in a prospective manner accounting for disease severity.

  10. Interleukin-7 Ameliorates Immune Dysfunction and Improves Survival in a 2-Hit Model of Fungal Sepsis

    OpenAIRE

    Unsinger, Jacqueline; Burnham, Carey-Ann D.; McDonough, Jacquelyn; Morre, Michel; Prakash, Priya S.; Caldwell, Charles C.; Dunne, W. Michael; Hotchkiss, Richard S.

    2012-01-01

    Background. Secondary hospital-acquired fungal infections are common in critically-ill patients and mortality remains high despite antimicrobial therapy. Interleukin-7 (IL-7) is a potent immunotherapeutic agent that improves host immunity and has shown efficacy in bacterial and viral models of infection. This study examined the ability of IL-7, which is currently in multiple clinical trials (including hepatitis and human immunodeficiency virus), to improve survival in a clinically relevant 2-...

  11. Modeling the long-term kinetics of Salmonella survival on dry pet food.

    Science.gov (United States)

    Lambertini, Elisabetta; Mishra, Abhinav; Guo, Miao; Cao, Huilin; Buchanan, Robert L; Pradhan, Abani K

    2016-09-01

    Due to multiple outbreaks and large-scale product recalls, Salmonella has emerged as a priority pathogen in dry pet food and treats. However, little data are available to quantify risks posed by these classes of products to both pets and their owners. Specifically, the kinetics of Salmonella survival on complex pet food matrices are not available. This study measured the long-term kinetics of Salmonella survival on a dry pet food under storage conditions commonly encountered during production, retail, and in households (aw Salmonella enterica cocktail of 12 strains isolated from dry pet foods and treats was used to inoculate commercial dry dog food. Salmonella was enumerated on non-selective (BHI) and selective (XLD and BS) media. Results at 570 days indicated an initial relatively rapid decline (up to 54 days), followed by a much slower extended decline phase. The Weibull model provided a satisfactory fit for time series of Log-transformed Salmonella counts from all three media (δ: mean 4.65 day/Log (CFU/g); p: mean 0.364 on BHI). This study provides a survival model that can be applied in quantitative risk assessment models. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis

    Science.gov (United States)

    Bakalov, Veli; Amathieu, Roland; Triba, Mohamed N.; Clément, Marie-Jeanne; Reyes Uribe, Laura; Le Moyec, Laurence; Kaynar, Ata Murat

    2016-01-01

    Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate. PMID:28009836

  13. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

    Science.gov (United States)

    Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G C; Löck, Steffen; Richter, Christian

    2017-10-16

    Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g. C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

  14. Peridural analgesia may affect long-term survival in patients with colorectal cancer after surgery (PACO-RAS-Study): an analysis of a cancer registry.

    Science.gov (United States)

    Holler, Julia P N; Ahlbrandt, Janko; Burkhardt, Ernst; Gruss, Marco; Röhrig, Rainer; Knapheide, Julia; Hecker, Andreas; Padberg, Winfried; Weigand, Markus A

    2013-12-01

    To determine the effect of peridural analgesia on long-term survival in patients who underwent surgical treatment of colorectal carcinoma. Clinical and animal studies suggest a potential benefit of peridural analgesia on morbidity and mortality after cancer surgery. The effect of peridural analgesia on long-term outcome after surgery for colorectal cancer remains undefined. From 2003 to 2009, there were 749 patients who underwent surgery for colorectal carcinoma under general anesthesia with or without peridural analgesia. Clinical data were reviewed retrospectively and analyzed with multivariate analysis and Kaplan-Meier plots. There were 442 patients who received peridural analgesia and 307 patients who did not receive peridural analgesia. A substantial survival benefit was observed in patients who received peridural analgesia (5-year survival rate: peridural analgesia, 62%; no peridural analgesia, 54%; P < 0.02). The hazard rate for death was decreased by 27% in patients who received peridural analgesia. When peridural analgesia was included simultaneously in a Cox model with the confounding factors age, American Society of Anesthesiologists classification, and stage, there was a significant survival benefit in patients who received peridural analgesia. In patients with America Society of Anesthesiologists classification 3 to 4, there was significantly greater survival with peridural analgesia than without peridural analgesia (P < 0.009). Peridural analgesia may improve survival in patients underwent surgery for colorectal carcinoma. The survival benefit with peridural analgesia was greater in patients who had greater medical morbidity.

  15. Analysis on Lung Cancer Survival from 2001 to 2007 in Qidong, China

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    Jian ZHU

    2011-01-01

    Full Text Available Background and objective Lung cancer is one of the most important malignancies in China. Survival rates of lung cancer on the population-based cancer registry for the years 2001-2007 in Qidong were analysed in order to provide the basis for the prognosis assessment and the control of this cancer. Methods Total 4,451 registered lung cancer cases was followed up to December 31st, 2009. Death certificates only (DCO cases were excluded, leaving 4,382 cases for survival analysis. Cumulative observed survival rate (OS and relative survival rate (RS were calculated using Hakulinen’s method performed by the SURV 3.01 software developed at the Finnish Cancer Registry. Results The 1-, 3-, and 5-year OS rates were 23.73%, 11.89%, 10.01%, and the RS rates were 24.86%, 13.69%, 12.73%, respectively. The 1-, 3-, and 5-year RS of males vs females were 23.70% vs 27.89%, 12.58% vs 16.53%, and 11.73% vs 15.21%, respectively, with statisitically significant differences (χ2=13.77, P=0.032. RS of age groups of 15-34, 35-44, 45-54, 55-64, 65-74 and 75+ were 35.46%, 17.66%, 11.97%, 13.49%, 10.61%, 15.14%, respectively. Remarkable improvement could be seen for the 5-year RS in this setting if compared with that for the years 1972-2000. Conclusion The lung cancer survival outcomes in Qidong have been improved gradually for the past decades. Further measures on the prevention, diagnosis and treatment of lung cancer should be taken.

  16. Operations and Modeling Analysis

    Science.gov (United States)

    Ebeling, Charles

    2005-01-01

    The Reliability and Maintainability Analysis Tool (RMAT) provides NASA the capability to estimate reliability and maintainability (R&M) parameters and operational support requirements for proposed space vehicles based upon relationships established from both aircraft and Shuttle R&M data. RMAT has matured both in its underlying database and in its level of sophistication in extrapolating this historical data to satisfy proposed mission requirements, maintenance concepts and policies, and type of vehicle (i.e. ranging from aircraft like to shuttle like). However, a companion analyses tool, the Logistics Cost Model (LCM) has not reached the same level of maturity as RMAT due, in large part, to nonexistent or outdated cost estimating relationships and underlying cost databases, and it's almost exclusive dependence on Shuttle operations and logistics cost input parameters. As a result, the full capability of the RMAT/LCM suite of analysis tools to take a conceptual vehicle and derive its operations and support requirements along with the resulting operating and support costs has not been realized.

  17. Integrative analysis of micro-RNA, gene expression, and survival of glioblastoma multiforme.

    Science.gov (United States)

    Huang, Yen-Tsung; Hsu, Thomas; Kelsey, Karl T; Lin, Chien-Ling

    2015-02-01

    Glioblastoma multiforme (GBM), the most common type of malignant brain tumor, is highly fatal. Limited understanding of its rapid progression necessitates additional approaches that integrate what is known about the genomics of this cancer. Using a discovery set (n = 348) and a validation set (n = 174) of GBM patients, we performed genome-wide analyses that integrated mRNA and micro-RNA expression data from GBM as well as associated survival information, assessing coordinated variability in each as this reflects their known mechanistic functions. Cox proportional hazards models were used for the survival analyses, and nonparametric permutation tests were performed for the micro-RNAs to investigate the association between the number of associated genes and its prognostication. We also utilized mediation analyses for micro-RNA-gene pairs to identify their mediation effects. Genome-wide analyses revealed a novel pattern: micro-RNAs related to more gene expressions are more likely to be associated with GBM survival (P = 4.8 × 10(-5)). Genome-wide mediation analyses for the 32,660 micro-RNA-gene pairs with strong association (false discovery rate [FDR] micro-RNAs and mediated their prognostic effects as well. We further constructed a gene signature using the 16 genes, which was highly associated with GBM survival in both the discovery and validation sets (P = 9.8 × 10(-6)). This comprehensive study discovered mediation effects of micro-RNA to gene expression and GBM survival and provided a new analytic framework for integrative genomics. © 2014 WILEY PERIODICALS, INC.

  18. Dedifferentiated chondrosarcoma: A survival analysis of 159 cases from the SEER database (2001-2011).

    Science.gov (United States)

    Strotman, Patrick K; Reif, Taylor J; Kliethermes, Stephanie A; Sandhu, Jasmin K; Nystrom, Lukas M

    2017-08-01

    Dedifferentiated chondrosarcoma is a rare malignancy with reported 5-year overall survival rates ranging from 7% to 24%. The purpose of this investigation is to determine the overall survival of dedifferentiated chondrosarcoma in a modern patient series and how it is impacted by patient demographics, tumor characteristics, and surgical treatment factors. This is a retrospective review of the Surveillance, Epidemiology, and End Results (SEER) database from 2001 to 2011. Kaplan Meier analyses were used for overall and disease-specific survival. Univariable and multivariable cox regression models were used to identify prognostic factors. Five year overall- and disease-specific survival was 18% (95% CI: 12-26%) and 28% (95% CI: 18-37%), respectively. Individuals with extremity tumors had a worse prognosis than individuals with a primary tumor in the chest wall or axial skeleton (HR 0.20, 95% CI: 0.07-0.56; P = 0.002 and HR 0.60, 95% CI: 0.36-0.99; P = 0.04, respectively). Patients with AJCC stage III or IV disease (HR 2.51, 95% CI: 1.50-4.20; P = 0.001), tumors larger than 8 cm (HR 2.17, 95% CI: 1.11-4.27; P = 0.046), metastatic disease at diagnosis (HR 3.25, 95% CI: 1.98-5.33; P chondrosarcoma is poor with a 5-year overall survival of 18%. Patients with a primary tumor located in the chest wall had a better prognosis. Tumors larger than 8 cm, presence of metastases at diagnosis, and treatment without surgical resection were significant predictors of mortality. © 2017 Wiley Periodicals, Inc.

  19. Discovery analysis of TCGA data reveals association between germline genotype and survival in ovarian cancer patients.

    Directory of Open Access Journals (Sweden)

    Rosemary Braun

    Full Text Available Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancers. This is attributable to the late stage at which the majority of ovarian cancers are diagnosed, coupled with the low and variable response of advanced tumors to standard chemotherapies. To date, clinically useful predictors of treatment response remain lacking. Identifying the genetic determinants of ovarian cancer survival and treatment response is crucial to the development of prognostic biomarkers and personalized therapies that may improve outcomes for the late-stage patients who comprise the majority of cases.To identify constitutional genetic variations contributing to ovarian cancer mortality, we systematically investigated associations between germline polymorphisms and ovarian cancer survival using data from The Cancer Genome Atlas Project (TCGA. Using stage-stratified Cox proportional hazards regression, we examined >650,000 SNP loci for association with survival. We additionally examined whether the association of significant SNPs with survival was modified by somatic alterations.Germline polymorphisms at rs4934282 (AGAP11/C10orf116 and rs1857623 (DNAH14 were associated with stage-adjusted survival (p= 1.12e-07 and 1.80e-07, FDR q= 1.2e-04 and 2.4e-04, respectively. A third SNP, rs4869 (C10orf116, was additionally identified as significant in the exome sequencing data; it is in near-perfect LD with rs4934282. The associations with survival remained significant when somatic alterations.Discovery analysis of TCGA data reveals germline genetic variations that may play a role in ovarian cancer survival even among late-stage cases. The significant loci are located near genes previously reported as having a possible relationship to platinum and taxol response. Because the variant alleles at the significant loci are common (frequencies for rs4934282 A/C alleles = 0.54/0.46, respectively; rs1857623 A/G alleles = 0

  20. Molecular Genetic Analysis of Human Endometrial Mesenchymal Stem Cells That Survived Sublethal Heat Shock

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    A. E. Vinogradov

    2017-01-01

    Full Text Available High temperature is a critical environmental and personal factor. Although heat shock is a well-studied biological phenomenon, hyperthermia response of stem cells is poorly understood. Previously, we demonstrated that sublethal heat shock induced premature senescence in human endometrial mesenchymal stem cells (eMSC. This study aimed to investigate the fate of eMSC-survived sublethal heat shock (SHS with special emphasis on their genetic stability and possible malignant transformation using methods of classic and molecular karyotyping, next-generation sequencing, and transcriptome functional analysis. G-banding revealed random chromosome breakages and aneuploidy in the SHS-treated eMSC. Molecular karyotyping found no genomic imbalance in these cells. Gene module and protein interaction network analysis of mRNA sequencing data showed that compared to untreated cells, SHS-survived progeny revealed some difference in gene expression. However, no hallmarks of cancer were found. Our data identified downregulation of oncogenic signaling, upregulation of tumor-suppressing and prosenescence signaling, induction of mismatch, and excision DNA repair. The common feature of heated eMSC is the silence of MYC, AKT1/PKB oncogenes, and hTERT telomerase. Overall, our data indicate that despite genetic instability, SHS-survived eMSC do not undergo transformation. After long-term cultivation, these cells like their unheated counterparts enter replicative senescence and die.

  1. Surviving blind decomposition: A distributional analysis of the time-course of complex word recognition.

    Science.gov (United States)

    Schmidtke, Daniel; Matsuki, Kazunaga; Kuperman, Victor

    2017-11-01

    The current study addresses a discrepancy in the psycholinguistic literature about the chronology of information processing during the visual recognition of morphologically complex words. Form-then-meaning accounts of complex word recognition claim that morphemes are processed as units of form prior to any influence of their meanings, whereas form-and-meaning models posit that recognition of complex word forms involves the simultaneous access of morphological and semantic information. The study reported here addresses this theoretical discrepancy by applying a nonparametric distributional technique of survival analysis (Reingold & Sheridan, 2014) to 2 behavioral measures of complex word processing. Across 7 experiments reported here, this technique is employed to estimate the point in time at which orthographic, morphological, and semantic variables exert their earliest discernible influence on lexical decision RTs and eye movement fixation durations. Contrary to form-then-meaning predictions, Experiments 1-4 reveal that surface frequency is the earliest lexical variable to exert a demonstrable influence on lexical decision RTs for English and Dutch derived words (e.g., badness; bad + ness), English pseudoderived words (e.g., wander; wand + er) and morphologically simple control words (e.g., ballad; ball + ad). Furthermore, for derived word processing across lexical decision and eye-tracking paradigms (Experiments 1-2; 5-7), semantic effects emerge early in the time-course of word recognition, and their effects either precede or emerge simultaneously with morphological effects. These results are not consistent with the premises of the form-then-meaning view of complex word recognition, but are convergent with a form-and-meaning account of complex word recognition. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Vitamin C and survival among women with breast cancer: a meta-analysis.

    Science.gov (United States)

    Harris, Holly R; Orsini, Nicola; Wolk, Alicja

    2014-05-01

    The association between dietary vitamin C intake and breast cancer survival is inconsistent and few studies have specifically examined vitamin C supplement use among women with breast cancer. The purpose of this study was to summarise results from prospective studies on the association between vitamin C supplement use and dietary vitamin C intake and breast cancer-specific mortality and total mortality. Studies were identified using the PubMed database through February 6, 2014 and by examining the references of retrieved articles. Prospective studies were included if they reported relative risks (RR) with 95% confidence intervals (95% CIs) for at least two categories or as a continuous exposure. Random-effects models were used to combine study-specific results. The ten identified studies examined vitamin C supplement use (n=6) and dietary vitamin C intake (n=7) and included 17,696 breast cancer cases, 2791 total deaths, and 1558 breast cancer-specific deaths. The summary RR (95% CI) for post-diagnosis vitamin C supplement use was 0.81 (95% CI 0.72-0.91) for total mortality and 0.85 (95% CI 0.74-0.99) for breast cancer-specific mortality. The summary RR for a 100mg per day increase in dietary vitamin C intake was 0.73 (95% CI 0.59-0.89) for total mortality and 0.78 (95% CI 0.64-0.94) for breast cancer-specific mortality. Results from this meta-analysis suggest that post-diagnosis vitamin C supplement use may be associated with a reduced risk of mortality. Dietary vitamin C intake was also statistically significantly associated with a reduced risk of total mortality and breast cancer-specific mortality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Survival of endometrial cancer patients in Germany in the early 21st century: a period analysis by age, histology, and stage

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    Chen Tianhui

    2012-03-01

    Full Text Available Abstract Background Population-based studies on endometrial cancer providing survival estimates by age, histology, and stage have been sparse. We aimed to derive most up-to-date and detailed survival estimates for endometrial cancer patients in Germany. Methods We used a pooled German national dataset including data from 11 cancer registries covering a population of 33 million people. 30,906 patients diagnosed with endometrial cancer in 1997-2006 were included. Period analysis was performed to calculate 5-year relative survival (RS in 2002-2006. Trends in survival between 2002 and 2006 were examined using model-based period analysis. Age-adjustment was performed using five age groups (15-44, 45-54, 55-64, 65-74, and 75+ years. Results Overall, age-adjusted 5-year relative survival in 2002-2006 was 81%. A moderate age gradient was observed, with 5-year RS decreasing from 90% in the age group 15-49 years to 75% in the age group 70+ years. Furthermore prognosis varied strongly by histologic subtypes and stage, with age-adjusted 5-year RS ranging from 43% (for sarcoma to 94% (for squamous metaplasia, and reaching 91% for localized, 51% for regional, and 20% for distant stage. Except for age group 65-74 years, no significant improvement in survival was seen during the recent 5-year period under investigation. Conclusion In this comprehensive population-based survival analysis of patients with endometrial cancer from Germany, prognosis of endometrial cancer moderately varied by age, and strongly varied by histology and stage. While prognosis is rather good overall, further improvement in 5-year relative survival of endometrial cancer patients has been stagnating in the early 21st century.

  4. Survival benefit with capecitabine/docetaxel versus docetaxel alone: analysis of therapy in a randomized phase III trial.

    Science.gov (United States)

    Miles, David; Vukelja, Svetislava; Moiseyenko, Vladimir; Cervantes, Guadalupe; Mauriac, Louis; Van Hazel, Guy; Liu, Wing-Yiu; Ayoub, Jean-Pierre; O'Shaughnessy, Joyce A

    2004-10-01

    In a large phase III trial of 511 patients with anthracycline-pretreated advanced/metastatic breast cancer, capecitabine/docetaxel combination therapy was shown to have significantly superior efficacy compared with single-agent docetaxel, including superior progression-free and overall survival and objective response rate. An updated survival analysis with >/= 27 months follow-up shows that patients receiving combination therapy maintained significantly superior survival (hazard ratio [HR], 0.777 [95% CI, 0.645-0.942]; P < 0.01; median survival, 14.5 months vs. 11.5 months) compared with those receiving single-agent docetaxel. Following the failure of docetaxel monotherapy, 35% of patients did not receive additional cytotoxic chemotherapy. Among patients randomized to single-agent docetaxel, only those given poststudy single-agent capecitabine had significantly prolonged survival compared with those given any other poststudy chemotherapy (HR, 0.500; P = 0.0046; median survival, 21.0 months vs. 12.3 months, respectively). By contrast, poststudy vinorelbine-containing chemotherapy did not affect survival following progression on single-agent docetaxel compared with other poststudy chemotherapy regimens (HR, 1.014; P = 0.94; median survival, 13.5 months vs. 12.6 months, respectively). Among patients randomized to combination therapy, discontinuing docetaxel of capecitabine has a similar effect on survival (HR, 0.720; P = 0.20; median survival, 15.8 months vs. 18.3 months, respectively). Median survival was 18.3 months in patients who discontinued docetaxel and continued to receive capecitabine versus 15.8 months in patients who discontinued capecitabine and continued to receive docetaxel, with a trend toward improved survival in patients continuing to receive capecitabine. Although this is a retrospective analysis, these data suggest that the sequential administration of docetaxel followed by capecitabine is associated with prolonged survival in patients who are

  5. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

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    Taktak, Azzam F G [Department of Clinical Engineering, Duncan Building, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom); Fisher, Anthony C [Department of Clinical Engineering, Duncan Building, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom); Damato, Bertil E [Department of Ophthalmology, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom)

    2004-01-07

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For <10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate.

  6. Leptin-deficient obesity prolongs survival in a murine model of myelodysplastic syndrome.

    Science.gov (United States)

    Kraakman, Michael J; Kammoun, Helene L; Dragoljevic, Dragana; Al-Sharea, Annas; Lee, Man K S; Flynn, Michelle C; Stolz, Christian J; Guirguis, Andrew A; Lancaster, Graeme I; Chin-Dusting, Jaye; Curtis, David J; Murphy, Andrew J

    2018-01-25

    Obesity enhances the risk of developing myelodysplastic syndromes. However, the effect of obesity on survival is unclear. Obese people present with monocytosis due to inflammatory signals emanating from obese adipose tissue. We hypothesized that obesity-induced myelopoiesis would promote the transition of myelodysplastic syndrome to acute myeloid leukemia and accelerate mortality in obesity. Obese Ob/Ob mice or their lean littermate controls received a bone marrow transplant from NUP98-HOXD13 transgenic mice, a model of myelodysplastic syndrome. The metabolic parameters of the mice were examined throughout the course of the study, as were blood leukocytes. Myeloid cells were analyzed in the bone, spleen, liver and adipose tissue by flow cytometry halfway through the disease progression and at the endpoint. Survival curves were also calculated. Contrary to our hypothesis, transplantation of NUP98-HOXD13 bone marrow into obese recipient mice significantly increased survival time compared with lean recipient controls. While monocyte skewing was exacerbated in obese mice receiving NUP98-HOXD13 bone marrow, transformation to acute myeloid leukemia was not enhanced. Increased survival of obese mice was associated with a preservation of fat mass as well as increased myeloid cell deposition within the adipose tissue and a concomitant reduction in detrimental myeloid cell accumulation within other organs. This study revealed that obesity increases survival in animals with myelodysplastic syndrome. This may be due to the greater fat mass of Ob/Ob mice, which acts as a sink for myeloid cells, preventing their accumulation in other key organs such as the liver. Copyright © 2018, Ferrata Storti Foundation.

  7. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

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    Oberije, Cary, E-mail: cary.oberije@maastro.nl [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); De Ruysscher, Dirk [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Universitaire Ziekenhuizen Leuven, KU Leuven (Belgium); Houben, Ruud [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Heuvel, Michel van de; Uyterlinde, Wilma [Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Deasy, Joseph O. [Memorial Sloan Kettering Cancer Center, New York (United States); Belderbos, Jose [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Dingemans, Anne-Marie C. [Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands); Rimner, Andreas; Din, Shaun [Memorial Sloan Kettering Cancer Center, New York (United States); Lambin, Philippe [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)

    2015-07-15

    Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

  8. Prognostic model for survival in patients with early stage cervical cancer.

    Science.gov (United States)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

    2011-02-15

    In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

  9. Survival rates of porcelain laminate restoration based on different incisal preparation designs: An analysis

    Science.gov (United States)

    Shetty, Ashish; Kaiwar, Anjali; Shubhashini, N; Ashwini, P; Naveen, DN; Adarsha, MS; Shetty, Mitha; Meena, N

    2011-01-01

    Background: Veneer restorations provide a valid conservative alternative to complete coverage as they avoid aggressive dental preparation; thus, maintaining tooth structure. Initially, laminates were placed on the unprepared tooth surface. Although there is as yet no consensus as to whether or not teeth should be prepared for laminate veneers, currently, more conservative preparations have been advocated. Because of their esthetic appeal, biocompatibility and adherence to the physiology of minimal-invasive dentistry, porcelain laminate veneers have now become a restoration of choice. Currently, there is a lack of clinical consensus regarding the type of design preferred for laminates. Widely varying survival rates and methods for its estimation have been reported for porcelain veneers over approximately 2–10 years. Relatively few studies have been reported in the literature that use survival estimates, which allow for valid study comparisons between the types of preparation designs used. No survival analysis has been undertaken for the designs used. The purpose of this article is to attempt to review the survival rates of veneers based on different incisal preparation designs from both clinical and non-clinical studies. Aims and Objectives: The purpose of this study is to review both clinical and non-clinical studies to determine the survival rates of veneers based on different incisal preparation designs. A further objective of the study is to understand which is the most successful design in terms of preparation. Materials and Methods This study evaluated the existing literature – survival rates of veneers based on incisal preparation designs. The search strategy involved MEDLINE, BITTORRENT and other databases. Statistical Analysis Data were tabulated. Because of variability in the follow-up period in different studies, the follow-up period was extrapolated to 10 years in common for all of them. Accordingly, the failure rate was then estimated and The

  10. Hypofractionated radiation therapy for invasive thyroid carcinoma in dogs: a retrospective analysis of survival.

    Science.gov (United States)

    Brearley, M J; Hayes, A M; Murphy, S

    1999-05-01

    Thirteen dogs with invasive thyroid carcinoma (WHO classification T2b or T3b) seen between January 1991 and October 1997 were treated by external beam irradiation. Four once-weekly fractions of 9 gray of 4 MeV X-rays were administered. Four of the dogs died of progression of the primary disease and four from metastatic spread. Of the remaining dogs, three died of unrelated problems, although two were still alive at the time of the censor. Kaplan-Meier analysis of the survival time from first dose to death from either primary or metastatic disease gave a median survival time of 96 weeks (mean 85 weeks, range six to 247 weeks). Radiographic evidence of pulmonary metastatic disease at presentation had no prognostic value whereas crude growth rate was a highly significant factor. The present series indicates that radiation therapy should be considered an important modality for the control of invasive thyroid carcinoma in the dog.

  11. Propranolol and survival from breast cancer: a pooled analysis of European breast cancer cohorts.

    Science.gov (United States)

    Cardwell, Chris R; Pottegård, Anton; Vaes, Evelien; Garmo, Hans; Murray, Liam J; Brown, Chris; Vissers, Pauline A J; O'Rorke, Michael; Visvanathan, Kala; Cronin-Fenton, Deirdre; De Schutter, Harlinde; Lambe, Mats; Powe, Des G; van Herk-Sukel, Myrthe P P; Gavin, Anna; Friis, Søren; Sharp, Linda; Bennett, Kathleen

    2016-12-01

    Preclinical studies have demonstrated that propranolol inhibits several pathways involved in breast cancer progression and metastasis. We investigated whether breast cancer patients who used propranolol, or other non-selective beta-blockers, had reduced breast cancer-specific or all-cause mortality in eight European cohorts. Incident breast cancer patients were identified from eight cancer registries and compiled through the European Cancer Pharmacoepidemiology Network. Propranolol and non-selective beta-blocker use was ascertained for each patient. Breast cancer-specific and all-cause mortality were available for five and eight cohorts, respectively. Cox regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (CIs) for cancer-specific and all-cause mortality by propranolol and non-selective beta-blocker use. HRs were pooled across cohorts using meta-analysis techniques. Dose-response analyses by number of prescriptions were also performed. Analyses were repeated investigating propranolol use before cancer diagnosis. The combined study population included 55,252 and 133,251 breast cancer patients in the analysis of breast cancer-specific and all-cause mortality respectively. Overall, there was no association between propranolol use after diagnosis of breast cancer and breast cancer-specific or all-cause mortality (fully adjusted HR = 0.94, 95% CI, 0.77, 1.16 and HR = 1.09, 95% CI, 0.93, 1.28, respectively). There was little evidence of a dose-response relationship. There was also no association between propranolol use before breast cancer diagnosis and breast cancer-specific or all-cause mortality (fully adjusted HR = 1.03, 95% CI, 0.86, 1.22 and HR = 1.02, 95% CI, 0.94, 1.10, respectively). Similar null associations were observed for non-selective beta-blockers. In this large pooled analysis of breast cancer patients, use of propranolol or non-selective beta-blockers was not associated with improved survival.

  12. Survival prediction from clinico-genomic models--a comparative study.

    Science.gov (United States)

    Bøvelstad, Hege M; Nygård, Ståle; Borgan, Ornulf

    2009-12-13

    Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models. We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/. Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets

  13. Iron-Chelating Drugs Enhance Cone Photoreceptor Survival in a Mouse Model of Retinitis Pigmentosa.

    Science.gov (United States)

    Wang, Ke; Peng, Bo; Xiao, Jia; Weinreb, Orly; Youdim, Moussa B H; Lin, Bin

    2017-10-01

    Retinitis pigmentosa (RP) is a group of hereditary retinal degeneration in which mutations commonly result in the initial phase of rod cell death followed by gradual cone cell death. The mechanisms by which the mutations lead to photoreceptor cell death in RP have not been clearly elucidated. There is currently no effective treatment for RP. The purpose of this work was to explore iron chelation therapy for improving cone survival and function in the rd10 mouse model of RP. Two iron-chelating drugs, 5-(4-(2-hydroxyethyl) piperazin-1-yl (methyl)-8-hydroxyquinoline (VK28) and its chimeric derivative 5-(N-methyl-N-propargyaminomethyl)-quinoline-8-oldihydrochloride (VAR10303), were injected intraperitoneally to rd10 mice every other day starting from postnatal day 14. We investigate the effects of the two compounds on cone rescue at three time points, using a combination of immunocytochemistry, RT-PCR, Western blot analysis, and a series of visual function tests. VK28 and VAR10303 treatments partially rescued cones, and significantly improved visual function in rd10 mice. Moreover, we showed that the neuroprotective effects of VK28 and VAR10303 were correlated to inhibition of neuroinflammation, oxidative stress, and apoptosis. Furthermore, we demonstrated that downregulation of NF-kB and p53 is likely to be the mechanisms by which proinflammatory mediators and apoptosis are reduced in the rd10 retina, respectively. VK28 and VAR10303 provided partial histologic and functional rescue of cones in RD10 mice. Our study demonstrated that iron chelation therapy might represent an effective therapeutic strategy for RP patients.

  14. Reduction of cardiac cell death after helium postconditioning in rats: transcriptional analysis of cell death and survival pathways.

    Science.gov (United States)

    Oei, Gezina T M L; Heger, Michal; van Golen, Rowan F; Alles, Lindy K; Flick, Moritz; van der Wal, Allard C; van Gulik, Thomas M; Hollmann, Markus W; Preckel, Benedikt; Weber, Nina C

    2015-01-20

    Helium, a noble gas, has been used safely in humans. In animal models of regional myocardial ischemia/reperfusion (I/R) it was shown that helium conditioning reduces infarct size. Currently, it is not known how helium exerts its cytoprotective effects and which cell death/survival pathways are affected. The objective of this study, therefore, was to investigate the cell protective effects of helium postconditioning by PCR array analysis of genes involved in necrosis, apoptosis and autophagy. Male rats were subjected to 25 min of ischemia and 5, 15 or 30 min of reperfusion. Semiquantitative histological analysis revealed that 15 min of helium postconditioning reduced the extent of I/R-induced cell damage. This effect was not observed after 5 and 30 min of helium postconditioning. Analysis of the differential expression of genes showed that 15 min of helium postconditioning mainly caused upregulation of genes involved in autophagy and inhibition of apoptosis versus I/R alone. The results suggest that the cytoprotective effects of helium inhalation may be caused by a switch from pro-cell-death signaling to activation of cell survival mechanisms, which appears to affect a wide range of pathways.

  15. Development of a model to predict breast cancer survival using data from the National Cancer Data Base.

    Science.gov (United States)

    Asare, Elliot A; Liu, Lei; Hess, Kenneth R; Gordon, Elisa J; Paruch, Jennifer L; Palis, Bryan; Dahlke, Allison R; McCabe, Ryan; Cohen, Mark E; Winchester, David P; Bilimoria, Karl Y

    2016-02-01

    With the large amounts of data on patient, tumor, and treatment factors available to clinicians, it has become critically important to harness this information to guide clinicians in discussing a patient's prognosis. However, no widely accepted survival calculator is available that uses national data and includes multiple prognostic factors. Our objective was to develop a model for predicting survival among patients diagnosed with breast cancer using the National Cancer Data Base (NCDB) to serve as a prototype for the Commission on Cancer's "Cancer Survival Prognostic Calculator." A retrospective cohort of patients diagnosed with breast cancer (2003-2006) in the NCDB was included. A multivariable Cox proportional hazards regression model to predict overall survival was developed. Model discrimination by 10-fold internal cross-validation and calibration was assessed. There were 296,284 patients for model development and internal validation. The c-index for the 10-fold cross-validation ranged from 0.779 to 0.788 after inclusion of all available pertinent prognostic factors. A plot of the observed versus predicted 5 year overall survival showed minimal deviation from the reference line. This breast cancer survival prognostic model to be used as a prototype for building the Commission on Cancer's "Cancer Survival Prognostic Calculator" will offer patients and clinicians an objective opportunity to estimate personalized long-term survival based on patient demographic characteristics, tumor factors, and treatment delivered. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. Modeling the survivability of brucella to exposure of Ultraviolet radiation and temperature

    Science.gov (United States)

    Howe, R.

    Accumulated summation of daily Ultra Violet-B (UV-B = 290? to 320 ? ) data? from The USDA Ultraviolet Radiation Monitoring Program show good correlation (R^2 = 77%) with daily temperature data during the five month period from February through June, 1998. Exposure of disease organisms, such as brucella to the effects of accumulated UV-B radiation, can be modeled for a 5 month period from February through June, 1998. Estimates of a lethal dosage for brucell of UV-B in the environment is dependent on minimum/maximum temperature and Solar Zenith Angle for the time period. The accumulated increase in temperature over this period also effects the decomposition of an aborted fetus containing brucella. Decomposition begins at some minimum daily temperature at 27 to 30 degrees C and peaks at 39 to 40C. It is useful to view the summation of temperature as a threshold for other bacteria growth, so that accumulated temperature greater than some value causes decomposition through competition with other bacteria and brucella die from the accumulated effects of UV-B, temperature and organism competition. Results of a study (Cook 1998) to determine survivability of brucellosis in the environment through exposure of aborted bovine fetuses show no one cause can be attributed to death of the disease agent. The combination of daily increase in temperature and accumulated UV-B radiation reveal an inverse correlation to survivability data and can be modeled as an indicator of brucella survivability in the environment in arid regions.

  17. Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Shuhei Kaneko

    2015-01-01

    Full Text Available In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso has been widely used to select genes that truly correlated with a patient’s survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV. However, this method can pass over (or fail to identify true positive genes (i.e., it identifies false negatives in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.

  18. Effect of natural hirudin on random pattern skin flap survival in a porcine model.

    Science.gov (United States)

    Zhao, H; Shi, Q; Sun, Z Y; Yin, G Q; Yang, H L

    2012-01-01

    The effect of local administration of hirudin on random pattern skin flap survival was investigated in a porcine model. Three random pattern skin flaps (4 × 14 cm) were created on each flank of five Chinese minipigs. The experimental group (10 flaps) received 20 antithrombin units of hirudin, injected subdermally into the distal half immediately after surgery and on days 1 and 2; a control group (10 flaps) was injected with saline and a sham group (10 flaps) was not injected. All flaps were followed for 10 days postoperatively. Macroscopically, the congested/necrotic length in the experimental group was significantly decreased compared with the other two groups by day 3. Histopathological evaluation revealed venous congestion and inflammation in the control and sham groups from day 1, but minimal changes in the experimental group. By day 10, the mean ± SD surviving area was significantly greater in the experimental group (67.6 ± 2.1%) than in the control (45.2 ± 1.4%) or sham (48.3 ± 1.1%) groups. Local administration of hirudin can significantly increase the surviving area in overdimensioned random pattern skin flaps, in a porcine model.

  19. Inelastic cross section and survival probabilities at the LHC in minijet models

    Science.gov (United States)

    Fagundes, Daniel A.; Grau, Agnes; Pancheri, Giulia; Shekhovtsova, Olga; Srivastava, Yogendra N.

    2017-09-01

    Recent results for the total and inelastic hadronic cross sections from LHC experiments are compared with predictions from a single-channel eikonal minijet model driven by parton density functions and from an empirical model. The role of soft gluon resummation in the infrared region in taming the rise of minijets and their contribution to the increase of the total cross sections at high energies are discussed. Survival probabilities at the LHC, whose theoretical estimates range from circa 10% to a few per mille, are estimated in this model and compared with results from QCD-inspired models and from multichannel eikonal models. We revisit a previous calculation and examine the origin of these discrepancies.

  20. Modular degradable dendrimers enable small RNAs to extend survival in an aggressive liver cancer model.

    Science.gov (United States)

    Zhou, Kejin; Nguyen, Liem H; Miller, Jason B; Yan, Yunfeng; Kos, Petra; Xiong, Hu; Li, Lin; Hao, Jing; Minnig, Jonathan T; Zhu, Hao; Siegwart, Daniel J

    2016-01-19

    RNA-based cancer therapies are hindered by the lack of delivery vehicles that avoid cancer-induced organ dysfunction, which exacerbates carrier toxicity. We address this issue by reporting modular degradable dendrimers that achieve the required combination of high potency to tumors and low hepatotoxicity to provide a pronounced survival benefit in an aggressive genetic cancer model. More than 1,500 dendrimers were synthesized using sequential, orthogonal reactions where ester degradability was systematically integrated with chemically diversified cores, peripheries, and generations. A lead dendrimer, 5A2-SC8, provided a broad therapeutic window: identified as potent [EC50 75 mg/kg dendrimer repeated dosing). Delivery of let-7 g microRNA (miRNA) mimic inhibited tumor growth and dramatically extended survival. Efficacy stemmed from a combination of a small RNA with the dendrimer's own negligible toxicity, therefore illuminating an underappreciated complication in treating cancer with RNA-based drugs.

  1. The effects of aspirated thickened water on survival and pulmonary injury in a rabbit model.

    Science.gov (United States)

    Nativ-Zeltzer, Nogah; Kuhn, Maggie A; Imai, Denise M; Traslavina, Ryan P; Domer, Amanda S; Litts, Juliana K; Adams, Brett; Belafsky, Peter C

    2018-02-01

    Liquid thickeners are one of the most frequently utilized treatment strategies for persons with oropharyngeal swallowing dysfunction. The effect of commercially available thickeners on lung injury is uncertain. The purpose of this study was to compare the effects of aspiration of water alone, xanthan gum (XG)-thickened water, and cornstarch (CS)-thickened water on survival and lung morphology in a rabbit model. Animal model. Prospective small animal clinical trial. Adult New Zealand White rabbits (n = 24) were divided into three groups of eight rabbits. The groups underwent 3 consecutive days of 1.5 mL/kg intratracheal instillation of water (n = 8), XG-thickened water (n = 8), and CS-thickened water (n = 8). The animals were euthanized on day 4, and survival and pulmonary histopathology were compared between groups. In all, 12.5% of rabbits (n = 8) instilled with CS-thickened water survived until the endpoint of the study (day 4). All animals instilled with water (n = 8) or XG-thickened water (n = 8) survived. A mild increase in intra-alveolar hemorrhage was observed for the animals instilled with CS-thickened water compared to the other groups (P thickened with XG resulted in greater pulmonary inflammation, pulmonary interstitial congestion, and alveolar edema than water alone (P thickened water are fatal, and that XG-thickened water is more injurious than aspirated water alone. Additional research is necessary to further delineate the dangers of aspirated thickened liquids. NA. Laryngoscope, 128:327-331, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  2. Extension of the survival dimensionality reduction algorithm to detect epistasis in competing risks models (SDR-CR).

    Science.gov (United States)

    Beretta, Lorenzo; Santaniello, Alessandro

    2013-02-01

    The discovery and the description of the genetic background of common human diseases is hampered by their complexity and dynamic behavior. Appropriate bioinformatic tools are needed to account all the facets of complex diseases and to this end we recently described the survival dimensionality reduction (SDR) algorithm in the effort to model gene-gene interactions in the context of survival analysis. When one event precludes the occurrence of another event under investigation in the 'competing risk model', survival algorithms require particular adjustment to avoid the risk of reporting wrong or biased conclusions. The SDR algorithm was modified to incorporate the cumulative incidence function as well as an adapted version of the Brier score for mutually exclusive outcomes, to better search for epistatic models in the competing risk setting. The applicability of the new SDR algorithm (SDR-CR) was evaluated using synthetic lifetime epistatic datasets with competing risks and on a dataset of scleroderma patients. The SDR-CR algorithms retains a satisfactory power to detect the causative variants in simulated datasets under different scenarios of sample size and degrees of type I or type II censoring. In the real-world dataset, SDR-CR was capable of detecting a significant interaction between the IL-1α C-889T and the IL-1β C-511T single-nucleotide polymorphisms to predict the occurrence of restrictive lung disease vs. isolated pulmonary hypertension. We provide an useful extension of the SDR algorithm to analyze epistatic interactions in the competing risk settings that may be of use to unveil the genetic background of complex human diseases. http://sourceforge.net/projects/sdrproject/files/. Copyright © 2012 Elsevier Inc. All rights reserved.

  3. Cardiopulmonary bypass (CPB) has no significant impact on survival in patients undergoing nephrectomy and level III-IV inferior vena cava thrombectomy; a multi-institutional analysis

    Science.gov (United States)

    Dall'Era, Marc A.; Durbin-Johnson, Blythe; Carballido, Joaquín A.; Chandrasekar, Thenappan; Chromecki, Thomas; Ciancio, Gaetano; Daneshmand, Siamak; Gontero, Paolo; Gonzalez, Javier; Haferkamp, Axel; Hohenfellner, Markus; Huang, William C.; Espinós, Estefania Linares; Mandel, Philipp; Martinez-Salamanca, Juan I.; Master, Viraj A.; McKiernan, James M.; Montorsi, Francesco; Novara, Giacomo; Pahernik, Sascha; Palou, Juan; Pruthi, Raj S.; Rodriguez-Faba, Oscar; Russo, Paul; Scherr, Douglas S.; Shariat, Shahrokh F.; Spahn, Martin; Terrone, Carlo; Vergho, Daniel; Wallen, Eric M.; Xylinas, Evanguelos; Zigeuner, Richard; Libertino, John A.; Evans, Christopher P.

    2016-01-01

    Purpose The impact of cardiopulmonary bypass (CPB) usage in level III-IV tumor thrombectomy on surgical and oncologic outcomes is unknown. We sought to determine the impact of cardiopulmonary bypass (CPB) on overall and cancer specific survival, as well as surgical complication rates, and immediate outcomes in patients undergoing nephrectomy and level III-IV tumor thrombectomy with or without CPB. Patients and Methods We retrospectively analyzed 362 patients with RCC and with level III or IV tumor thrombus from 1992 to 2012 in 22 US and European centers. Cox proportional hazards models were used to compare overall and cancer-specific survival between patients with and without CPB. Perioperative mortality and complications rates were assessed using logistic regression analyses. Results The median overall survival was 24.6 months in non-CPB patients and 26.6 months in CPB patients. Overall survival and cancer-specific survival (CSS) did not differ significantly in both groups, neither in univariate analysis nor when adjusting for known risk factors. In multivariate analysis, no significant differences were seen in hospital LOS, Clavien 1-4 complication rate, intraoperative or 30 day mortality, and CSS between both groups. Limitations include the retrospective nature of the study. Conclusions In our multi-institutional analysis, the use of cardiopulmonary bypass did not significantly impact cancer specific survival or overall survival in patients undergoing nephrectomy and level III or IV tumor thrombectomy. Neither approach was independently associated with increased mortality in the multivariate analysis. Higher surgical complications were not independently associated with the use of CPB. PMID:25797392

  4. Integrative analysis of survival-associated gene sets in breast cancer.

    Science.gov (United States)

    Varn, Frederick S; Ung, Matthew H; Lou, Shao Ke; Cheng, Chao

    2015-03-12

    Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used

  5. Survival rates of porcelain laminate restoration based on different incisal preparation designs: An analysis.

    Science.gov (United States)

    Shetty, Ashish; Kaiwar, Anjali; Shubhashini, N; Ashwini, P; Naveen, Dn; Adarsha, Ms; Shetty, Mitha; Meena, N

    2011-01-01

    Veneer restorations provide a valid conservative alternative to complete coverage as they avoid aggressive dental preparation; thus, maintaining tooth structure. Initially, laminates were placed on the unprepared tooth surface. Although there is as yet no consensus as to whether or not teeth should be prepared for laminate veneers, currently, more conservative preparations have been advocated. Because of their esthetic appeal, biocompatibility and adherence to the physiology of minimal-invasive dentistry, porcelain laminate veneers have now become a restoration of choice. Currently, there is a lack of clinical consensus regarding the type of design preferred for laminates. Widely varying survival rates and methods for its estimation have been reported for porcelain veneers over approximately 2-10 years. Relatively few studies have been reported in the literature that use survival estimates, which allow for valid study comparisons between the types of preparation designs used. No survival analysis has been undertaken for the designs used. The purpose of this article is to attempt to review the survival rates of veneers based on different incisal preparation designs from both clinical and non-clinical studies. The purpose of this study is to review both clinical and non-clinical studies to determine the survival rates of veneers based on different incisal preparation designs. A further objective of the study is to understand which is the most successful design in terms of preparation. This study evaluated the existing literature - survival rates of veneers based on incisal preparation designs. The search strategy involved MEDLINE, BITTORRENT and other databases. Data were tabulated. Because of variability in the follow-up period in different studies, the follow-up period was extrapolated to 10 years in common for all of them. Accordingly, the failure rate was then estimated and The weighted mean was computed. The study found that the window preparation was of the

  6. Deeper model endgame analysis

    OpenAIRE

    Andrist, R.B.; Haworth, G.McC.

    2005-01-01

    A reference model of Fallible Endgame Play has been implemented and exercised with the chess-engine WILHELM. Past experiments have demonstrated the value of the model and the robustness of decisions based on it: experiments agree well with a Markov Model theory. Here, the reference model is exercised on the well-known endgame KBBKN.

  7. Survival analysis of patients with interval cancer undergoing gastric cancer screening by endoscopy.

    Directory of Open Access Journals (Sweden)

    Chisato Hamashima

    Full Text Available Interval cancer is a key factor that influences the effectiveness of a cancer screening program. To evaluate the impact of interval cancer on the effectiveness of endoscopic screening, the survival rates of patients with interval cancer were analyzed.We performed gastric cancer-specific and all-causes survival analyses of patients with screen-detected cancer and patients with interval cancer in the endoscopic screening group and radiographic screening group using the Kaplan-Meier method. Since the screening interval was 1 year, interval cancer was defined as gastric cancer detected within 1 year after a negative result. A Cox proportional hazards model was used to investigate the risk factors associated with gastric cancer-specific and all-causes death.A total of 1,493 gastric cancer patients (endoscopic screening group: n = 347; radiographic screening group: n = 166; outpatient group: n = 980 were identified from the Tottori Cancer Registry from 2001 to 2008. The gastric cancer-specific survival rates were higher in the endoscopic screening group than in the radiographic screening group and the outpatients group. In the endoscopic screening group, the gastric cancer-specific survival rate of the patients with screen-detected cancer and the patients with interval cancer were nearly equal (P = 0.869. In the radiographic screening group, the gastric cancer-specific survival rate of the patients with screen-detected cancer was higher than that of the patients with interval cancer (P = 0.009. For gastric cancer-specific death, the hazard ratio of interval cancer in the endoscopic screening group was 0.216 for gastric cancer death (95%CI: 0.054-0.868 compared with the outpatient group.The survival rate and the risk of gastric cancer death among the patients with screen-detected cancer and patients with interval cancer were not significantly different in the annual endoscopic screening. These results suggest the potential of endoscopic screening in

  8. Thermal analysis of ice and glass transitions in insects that do and do not survive freezing.

    Science.gov (United States)

    Rozsypal, Jan; Moos, Martin; Šimek, Petr; Koštál, Vladimír

    2018-03-01

    Some insects rely on the strategy of freeze tolerance for winter survival. During freezing, extracellular body water transitions from the liquid to solid phase and cells undergo freeze-induced dehydration. Here we present results of a thermal analysis (from differential scanning calorimetry) of ice fraction dynamics during gradual cooling after inoculative freezing in variously acclimated larvae of two drosophilid flies, Drosophila melanogaster and Chymomyza costata. Although the species and variants ranged broadly between 0 and close to 100% survival of freezing, there were relatively small differences in ice fraction dynamics. For instance, the maximum ice fraction (IF max ) ranged between 67.9 and 77.7% total body water (TBW). The C. costata larvae showed statistically significant phenotypic shifts in parameters of ice fraction dynamics (melting point and IF max ) upon entry into diapause, cold-acclimation, and feeding on a proline-augmented diet. These differences were mostly driven by colligative effects of accumulated proline (ranging between 6 and 487 mmol.kg -1 TBW) and other metabolites. Our data suggest that these colligative effects per se do not represent a sufficient mechanistic explanation for high freeze tolerance observed in diapausing, cold-acclimated C. costata larvae. Instead, we hypothesize that accumulated proline exerts its protective role via a combination of mechanisms. Specifically, we found a tight association between proline-induced stimulation of glass transition in partially-frozen body liquids (vitrification) and survival of cryopreservation in liquid nitrogen. © 2018. Published by The Company of Biologists Ltd.

  9. Squamous cell carcinoma of the pancreas: A systematic review and pooled survival analysis.

    Science.gov (United States)

    Ntanasis-Stathopoulos, Ioannis; Tsilimigras, Diamantis I; Georgiadou, Despoina; Kanavidis, Prodromos; Riccioni, Olga; Salla, Charitini; Psaltopoulou, Theodora; Sergentanis, Theodoros N

    2017-07-01

    The diagnosis and treatment of squamous cell carcinoma of the pancreas pose dilemmas in the clinical practice. The present study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eligible articles were sought in MEDLINE up to 30th April 2016. A pooled Cox regression analysis was performed to evaluate factors potentially associated with overall survival (OS) and relapse-free survival (RFS). Fifty-four cases of pure squamous cell pancreatic carcinomas were identified in total. The mean age was 61.9 years, and most patients were males (61.1%). The median OS was 7 months. Resectability (p = 0.003) and more recent publication year (p < 0.001) were associated with better OS, as was low/intermediate tumour grade (p = 0.032) with RFS. Despite its poor prognosis, survival rates of pancreatic squamous cell carcinoma seem improved during the recent years; resectability and low/intermediate grade emerged as favourable prognostic factors. Collaborative epidemiological studies are deemed necessary to further validate the results stemming from the published case reports of this rare entity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival

    Science.gov (United States)

    Desrichard, Alexis; Şenbabaoğlu, Yasin; Hakimi, A. Ari; Makarov, Vladimir; Reis-Filho, Jorge S.; Chan, Timothy A.

    2016-01-01

    As tumors accumulate genetic alterations, an evolutionary process occurs in which genetically distinct subclonal populations of cells co-exist, resulting in intratumor genetic heterogeneity (ITH). The clinical implications of ITH remain poorly defined. Data are limited with respect to whether ITH is an independent determinant of patient survival outcomes, across different cancer types. Here, we report the results of a pan-cancer analysis of over 3300 tumors, showing a varied landscape of ITH across 9 cancer types. While some gene mutations are subclonal, the majority of driver gene mutations are clonal events, present in nearly all cancer cells. Strikingly, high levels of ITH are associated with poorer survival across diverse types of cancer. The adverse impact of high ITH is independent of other clinical, pathologic and molecular factors. High ITH tends to be associated with lower levels of tumor-infiltrating immune cells, but this association is not able to explain the observed survival differences. Together, these data show that ITH is a prognostic marker in multiple cancers. These results illuminate the natural history of cancer evolution, indicating that tumor heterogeneity represents a significant obstacle to cancer control. PMID:26840267

  11. Turnover of new graduate nurses in their first job using survival analysis.

    Science.gov (United States)

    Cho, Sung-Hyun; Lee, Ji Yun; Mark, Barbara A; Yun, Sung-Cheol

    2012-03-01

    To examine factors related to turnover of new graduate nurses in their first job. Data were obtained from a 3-year panel survey (2006-2008) of the Graduates Occupational Mobility Survey that followed-up college graduates in South Korea. The sample consisted of 351 new graduates whose first job was as a full-time registered nurse in a hospital. Survival analysis was conducted to estimate survival curves and related factors, including individual and family, nursing education, hospital, and job dissatisfaction (overall and 10 specific job aspects). The estimated probabilities of staying in their first job for 1, 2, and 3 years were 0.823, 0.666, and 0.537, respectively. Nurses reporting overall job dissatisfaction had significantly lower survival probabilities than those who reported themselves to be either neutral or satisfied. Nurses were more likely to leave if they were married or worked in small (vs. large), nonmetropolitan, and nonunionized hospitals. Dissatisfaction with interpersonal relationships, work content, and physical work environment was associated with a significant increase in the hazards of leaving the first job. Hospital characteristics as well as job satisfaction were significantly associated with new graduates' turnover. The high turnover of new graduates could be reduced by improving their job satisfaction, especially with interpersonal relationships, work content, and the physical work environment. © 2012 Sigma Theta Tau International.

  12. Intratumoral delivery of bortezomib: impact on survival in an intracranial glioma tumor model.

    Science.gov (United States)

    Wang, Weijun; Cho, Hee-Yeon; Rosenstein-Sisson, Rachel; Marín Ramos, Nagore I; Price, Ryan; Hurth, Kyle; Schönthal, Axel H; Hofman, Florence M; Chen, Thomas C

    2017-04-14

    OBJECTIVE Glioblastoma (GBM) is the most prevalent and the most aggressive of primary brain tumors. There is currently no effective treatment for this tumor. The proteasome inhibitor bortezomib is effective for a variety of tumors, but not for GBM. The authors' goal was to demonstrate that bortezomib can be effective in the orthotopic GBM murine model if the appropriate method of drug delivery is used. In this study the Alzet mini-osmotic pump was used to bring the drug directly to the tumor in the brain, circumventing the blood-brain barrier; thus making bortezomib an effective treatment for GBM. METHODS The 2 human glioma cell lines, U87 and U251, were labeled with luciferase and used in the subcutaneous and intracranial in vivo tumor models. Glioma cells were implanted subcutaneously into the right flank, or intracranially into the frontal cortex of athymic nude mice. Mice bearing intracranial glioma tumors were implanted with an Alzet mini-osmotic pump containing different doses of bortezomib. The Alzet pumps were introduced directly into the tumor bed in the brain. Survival was documented for mice with intracranial tumors. RESULTS Glioma cells were sensitive to bortezomib at nanomolar quantities in vitro. In the subcutaneous in vivo xenograft tumor model, bortezomib given intravenously was effective in reducing tumor progression. However, in the intracranial glioma model, bortezomib given systemically did not affect survival. By sharp contrast, animals treated with bortezomib intracranially at the tumor site exhibited significantly increased survival. CONCLUSIONS Bypassing the blood-brain barrier by using the osmotic pump resulted in an increase in the efficacy of bortezomib for the treatment of intracranial tumors. Thus, the intratumoral administration of bortezomib into the cranial cavity is an effective approach for glioma therapy.

  13. Practical considerations when analyzing discrete survival times using the grouped relative risk model.

    Science.gov (United States)

    Altman, Rachel MacKay; Henrey, Andrew

    2017-10-11

    The grouped relative risk model (GRRM) is a popular semi-parametric model for analyzing discrete survival time data. The maximum likelihood estimators (MLEs) of the regression coefficients in this model are often asymptotically efficient relative to those based on a more restrictive, parametric model. However, in settings with a small number of sampling units, the usual properties of the MLEs are not assured. In this paper, we discuss computational issues that can arise when fitting a GRRM to small samples, and describe conditions under which the MLEs can be ill-behaved. We find that, overall, estimators based on a penalized score function behave substantially better than the MLEs in this setting and, in particular, can be far more efficient. We also provide methods of assessing the fit of a GRRM to small samples.

  14. Cell survival in carbon beams - comparison of amorphous track model predictions

    DEFF Research Database (Denmark)

    Grzanka, L.; Greilich, S.; Korcyl, M.

    neutrons, stopped pions, and heavy ion beams. Nucl Instrum Meth. 1973;111:93-116. 2.Weyrather WK, Kraft G. RBE of carbon ions: experimental data and the strategy of RBE calculation for treatment planning. Radiother Oncol. 2004;73(Suppl 2):161-9. 3.Greilich S, Grzanka L, Bassler N, Andersen CE, Jäkel O......Introduction: Predictions of the radiobiological effectiveness (RBE) play an essential role in treatment planning with heavy charged particles. Amorphous track models ( [1] , [2] , also referred to as track structure models) provide currently the most suitable description of cell survival under ion....... [2] . In addition, a new approach based on microdosimetric distributions is presented and investigated [3] . Material and methods: A suitable software library embrasing the mentioned amorphous track models including numerous submodels with respect to delta-electron range models, radial dose...

  15. Comparative transcriptome analysis quantifies immune cell transcript levels, metastatic progression and survival in osteosarcoma.

    Science.gov (United States)

    Scott, Milcah C; Temiz, Nuri A; Sarver, Anne E; LaRue, Rebecca S; Rathe, Susan K; Varshney, Jyotika; Wolf, Natalie K; Moriarity, Branden S; O'Brien, Timothy D; Spector, Logan G; Largaespada, David A; Modiano, Jaime F; Subramanian, Subbaya; Sarver, Aaron L

    2017-10-24

    Overall survival of patients with osteosarcoma (OS) has improved little in the past three decades and better models for study are needed. OS is common in large dog breeds and is genetically inducible in mice, making the disease ideal for comparative genomic analyses across species. Understanding the level of conservation of inter-tumor transcriptional variation across species and how it is associated with progression to metastasis will enable us to more efficiently develop effective strategies to manage OS and improve therapy. In this study, transcriptional profiles of OS tumors and cell lines derived from humans (n=49), mice (n=103) and dogs (n=34) were generated using RNA-sequencing. Conserved inter-tumor transcriptional variation was present in tumor sets from all three species and comprised gene clusters associated with cell cycle and mitosis and with the presence or absence of immune cells. Further, we developed a novel Gene Cluster Expression Summary Score (GCESS) to quantify inter-tumor transcriptional variation and demonstrated that these GCESS values associated with patient outcome. Human OS tumors with GCESS values suggesting decreased immune cell presence were associated with metastasis and poor survival. We validated these results in an independent human OS tumor cohort and in 15 different tumor data sets obtained from The Cancer Genome Atlas (TCGA). Our results suggest that quantification of immune cell absence and tumor cell proliferation may better inform therapeutic decisions and improve overall survival for OS patients. Copyright ©2017, American Association for Cancer Research.

  16. Inhibition of CDK4/6 by Palbociclib Significantly Extends Survival in Medulloblastoma Patient-Derived Xenograft Mouse Models.

    Science.gov (United States)

    Cook Sangar, Michelle L; Genovesi, Laura A; Nakamoto, Madison W; Davis, Melissa J; Knobluagh, Sue E; Ji, Pengxiang; Millar, Amanda; Wainwright, Brandon J; Olson, James M

    2017-10-01

    Purpose: Bioinformatics analysis followed by in vivo studies in patient-derived xenograft (PDX) models were used to identify and validate CDK 4/6 inhibition as an effective therapeutic strategy for medulloblastoma, particularly group 3 MYC-amplified tumors that have the worst clinical prognosis.Experimental Design: A protein interaction network derived from a Sleeping Beauty mutagenesis model of medulloblastoma was used to identify potential novel therapeutic targets. The top hit from this analysis was validated in vivo using PDX models of medulloblastoma implanted subcutaneously in the flank and orthotopically in the cerebellum of mice.Results: Informatics analysis identified the CDK4/6/CYCLIN D/RB pathway as a novel "druggable" pathway for multiple subgroups of medulloblastoma. Palbociclib, a highly specific inhibitor of CDK4/6, was found to inhibit RB phosphorylation and cause G1 arrest in PDX models of medulloblastoma. The drug caused rapid regression of Sonic hedgehog (SHH) and MYC-amplified group 3 medulloblastoma subcutaneous tumors and provided a highly significant survival advantage to mice bearing MYC-amplified intracranial tumors.Conclusions: Inhibition of CDK4/6 is potentially a highly effective strategy for the treatment of SHH and MYC-amplified group 3 medulloblastoma. Clin Cancer Res; 23(19); 5802-13. ©2017 AACR. ©2017 American Association for Cancer Research.

  17. Partial lateral facetectomy plus Insall's procedure for the treatment of isolated patellofemoral osteoarthritis: survival analysis.

    Science.gov (United States)

    Montserrat, Ferran; Alentorn-Geli, Eduard; León, Vicente; Ginés-Cespedosa, Alberto; Rigol, Pau

    2014-01-01

    The purpose of this study was to report the survival analysis of partial lateral facetectomy and Insall's procedure in patients with isolated patellofemoral osteoarthritis, and to assess the risk and protective factors for failure of this procedure. From 1992 to 2004, all subjects with isolated patellofemoral osteoarthritis who met the inclusion criteria and underwent this procedure were enrolled. Risk and protective factors for failure (failure considered as the need for total knee arthroplasty) were assessed by comparing obtained baseline data between failed and non-failed cases. Eighty-seven cases (mean (SD) age 61.8 (7.7) years, mean (SD) follow-up 9.6 (3.2) years) were included. Twenty-three failed cases were found. Mean (SD) survival time was 13.6 (0.5) years. At 13 years (last failure case), the cumulative survival was 59.3 %. Baseline medial tibiofemoral pain, genu flexum, and worst grade of tibiofemoral osteoarthritis were significant risk factors for failure (p < 0.0001, p = 0.02, p < 0.0001, respectively). In contrast, higher anatomical (p = 0.02) and total (p = 0.03) knee society score (KSS) scores, absence of knee effusion (p = 0.03), higher value of the Caton-Deschamps index (p = 0.03), and lateral position of the patella (p = 0.01) were all protective factors against failure. The treatment for isolated patellofemoral osteoarthritis through partial lateral facetectomy and Insall's procedure demonstrated good long-term survival. The presence of preoperative medial tibiofemoral pain, genu flexum, and incipient tibiofemoral osteoarthritis increased the risk of failure of this procedure. In contrast, higher anatomical and total KSS scores, absence of knee effusion, higher value of the Caton-Deschamps index, and lateral position of the patella were found to protect against failure.

  18. Survival Analysis in Patients with Non- metastatic Squamous Cell Carcinoma of the Urinary Bladder

    Directory of Open Access Journals (Sweden)

    Ahmed M. Abdel-Rahim

    2011-04-01

    Full Text Available Background: We conducted a retrospective analysis to evaluate overall survival(OAS and disease free survival (DFS rates in patients with squamous cell carcinoma of the urinary bladder according to different prognostic factors. Methods: This retrospective study analyzed the medical records of patients with non-metastatic squamous cell carcinoma of the urinary bladder. All men underwent radical cystectomy and women underwent anterior pelvic exentration. Most patients had postoperative radiation therapy. The log-rank test examined differences in OASand DFS rates. Results: The medical records of 106 patients were analyzed. The median follow-up from the date of enrollment was 30 months and ranged from 2 to 73 months. For the entire group, three-year OAS rates were 46.9% and DFS rates were 44%. For patients with P2 (tumor invasion into the muscularis propria the three-year OAS rate was 53%, for P3 (tumor invasion into perivesical fat it was 45% and 9% for P4 (tumor invasion into adjacent organs, pelvic wall or abdominal wall The OAS rate was statistically significant in favor of P2 disease (P=0.0041. The three-year DFS rate was 50% for P2, 45% for P3 and 9% for P4 disease (P=0.0125. Administration of post-operative radiotherapy did not result in statistically significant improvement in three-year OASand DFS rates. Conclusion: Survival rates were statistically significant and higher in patients with P2 and P3 disease compared to P4 disease. Adjuvant radiotherapy did not result in statistically significant survival improvement.

  19. Analysis of Survival After Initiation of Continuous Renal Replacement Therapy in a Surgical Intensive Care Unit.

    Science.gov (United States)

    Tatum, James M; Barmparas, Galinos; Ko, Ara; Dhillon, Navpreet; Smith, Eric; Margulies, Daniel R; Ley, Eric J

    2017-10-01

    Continuous renal replacement therapy (CRRT) benefits patients with renal failure who are too hemodynamically unstable for intermittent hemodialysis. The duration of therapy beyond which continued use is futile, particularly in a population of patients admitted to and primarily cared for by a surgical service (hereinafter referred to as surgical patients), is unclear. To analyze proportions of and independent risk factors for survival to discharge after initiation of CRRT among patients in a surgical intensive care unit (SICU). This retrospective cohort study included all patients undergoing CRRT from July 1, 2012, through January 31, 2016, in an SICU of an urban tertiary medical center. The population included patients treated before or after general surgery and patients admitted to a surgical service during inpatient evaluation and care before liver transplant. The pretransplant population was censored from further survival analysis on receipt of a transplant. Continuous renal replacement therapy. Hospital mortality among patients in an SICU after initiation of CRRT. Of 108 patients (64 men [59.3%] and 44 women [40.7%]; mean [SD] age, 62.0 [12.7] years) admitted to the SICU, 53 were in the general surgical group and 55 in the pretransplant group. Thirteen of the 22 patients in the pretransplant group who required 7 or more days of CRRT died (in-hospital mortality, 59.1%); among the 12 patients in the general surgery group who required 7 or more days of CRRT, 12 died (in-hospital mortality, 100%). In the general surgical group, each day of CRRT was associated with an increased adjusted odds ratio of death of 1.39 (95% CI, 1.01-1.90; P = .04). Continuous renal replacement therapy is valuable for surgical patients with an acute and correctable indication; however, survival decreases significantly with increasing duration of CRRT. Duration of CRRT does not correlate with survival among patients awaiting liver transplant.

  20. Survival of melanoma patients treated with novel drugs: retrospective analysis of real-world data.

    Science.gov (United States)

    Polkowska, Marta; Ekk-Cierniakowski, Paweł; Czepielewska, Edyta; Wysoczański, Wojciech; Matusewicz, Wojciech; Kozłowska-Wojciechowska, Małgorzata

    2017-10-01

    Recently, several new drugs have been licensed for advanced melanoma therapy, significantly changing the therapeutic landscape. Ipilimumab and vemurafenib were the first drugs that demonstrated a survival benefit over the long-standing standard therapy with dacarbazine. However, the comparative efficacy of these novel drugs has not been properly assessed yet. We conducted a retrospective analysis of all the Polish population treated between January 2012 and October 2016 with one of the following agents: ipilimumab (IPI), vemurafenib (VEM), dabrafenib (DAB), and classic chemotherapy (CTH). The main objective was to assess the overall survival of melanoma patients treated in real-world conditions, taking into account sequences of treatment. We identified 3397 patients with malignant melanoma treated for the first line and the second line. Patients receiving CTH were significantly older than those treated with the novel drugs. At the same time, the population treated with immunotherapy and targeted therapy was well balanced. Overall survival was significantly better for the novel drugs compared to classic chemotherapy in both lines (for the first line, VEM vs CTH HR = 0.72, 95% CI 0.65-0.81; p melanoma provide a significant advantage in survival over classic chemotherapy. Comparative assessment of IPI and VEM indicated no difference, but only immunotherapy-treated patients achieved long-lasting results. Our data on sequential treatment indicate that immunotherapy might be a better option for the first line rather than targeted therapy, but that conclusion requires further studies of the best way to manage the treatment of melanoma patients.

  1. Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma

    Science.gov (United States)

    Beig, Niha; Patel, Jay; Prasanna, Prateek; Partovi, Sasan; Varadan, Vinay; Madabhushi, Anant; Tiwari, Pallavi

    2017-03-01

    Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a median survival of 14 months. Hypoxia is a hallmark trait in GBM that is known to be associated with angiogenesis, tumor growth, and resistance to conventional therapy, thereby limiting treatment options for GBM patients. There is thus an urgent clinical need for non-invasively capturing tumor hypoxia in GBM towards identifying a subset of patients who would likely benefit from anti-angiogenic therapies (bevacizumab) in the adjuvant setting. In this study, we employed radiomic descriptors to (a) capture molecular variations of tumor hypoxia on routine MRI that are otherwise not appreciable; and (b) employ the radiomic correlates of hypoxia to discriminate patients with short-term survival (STS, overall survival (OS) 16 months). A total of 97 studies (25 STS, 36 MTS, 36 LTS) with Gadolinium T1-contrast (Gd-T1c), T2w, and FLAIR protocols with their corresponding gene expression profiles were obtained from the cancer genome atlas (TCGA) database. For each MRI study, necrotic, enhancing tumor, and edematous regions were segmented by an expert. A total of 30 radiomic descriptors (i.e. Haralick, Laws energy, Gabor) were extracted from every region across all three MRI protocols. By performing unsupervised clustering of the expression profile of hypoxia associated genes, a "low", "medium", or "high" index was defined for every study. Spearman correlation was then used to identify the most significantly correlated MRI features with the hypoxia index for every study. These features were further used to categorize each study as STS, MTS, and LTS using Kaplan-Meier (KM) analysis. Our results revealed that the most significant features (p < 0.05) were identified as Laws energy and Haralick features that capture image heterogeneity on FLAIR and Gd-T1w sequences. We also found these radiomic features to be significantly associated with survival, distinguishing MTS from LTS (p=.005) and STS from LTS (p=.0008).

  2. Pathway analysis reveals common pro-survival mechanisms of metyrapone and carbenoxolone after traumatic brain injury.

    Directory of Open Access Journals (Sweden)

    Helen L Hellmich

    Full Text Available Developing new pharmacotherapies for traumatic brain injury (TBI requires elucidation of the neuroprotective mechanisms of many structurally and functionally diverse compounds. To test our hypothesis that diverse neuroprotective drugs similarly affect common gene targets after TBI, we compared the effects of two drugs, metyrapone (MT and carbenoxolone (CB, which, though used clinically for noncognitive conditions, improved learning and memory in rats and humans. Although structurally different, both MT and CB inhibit a common molecular target, 11β hydroxysteroid dehydrogenase type 1, which converts inactive cortisone to cortisol, thereby effectively reducing glucocorticoid levels. We examined injury-induced signaling pathways to determine how the effects of these two compounds correlate with pro-survival effects in surviving neurons of the injured rat hippocampus. We found that treatment of TBI rats with MT or CB acutely induced in hippocampal neurons transcriptional profiles that were remarkably similar (i.e., a coordinated attenuation of gene expression across multiple injury-induced cell signaling networks. We also found, to a lesser extent, a coordinated increase in cell survival signals. Analysis of injury-induced gene expression altered by MT and CB provided additional insight into the protective effects of each. Both drugs attenuated expression of genes in the apoptosis, death receptor and stress signaling pathways, as well as multiple genes in the oxidative phosphorylation pathway such as subunits of NADH dehydrogenase (Complex1, cytochrome c oxidase (Complex IV and ATP synthase (Complex V. This suggests an overall inhibition of mitochondrial function. Complex 1 is the primary source of reactive oxygen species in the mitochondrial oxidative phosphorylation pathway, thus linking the protective effects of these drugs to a reduction in oxidative stress. The net effect of the drug-induced transcriptional changes observed here indicates that

  3. Survival analysis of pure seminoma at post-chemotherapy retroperitoneal lymph node dissection.

    Science.gov (United States)

    Rice, Kevin R; Beck, Stephen D W; Bihrle, Richard; Cary, K Clint; Einhorn, Lawrence H; Foster, Richard S

    2014-11-01

    Viable seminoma encountered at post-chemotherapy retroperitoneal lymph node dissection for pure testicular seminoma is rare due to the chemosensitivity of this germ cell tumor. In this study we define the natural history of viable seminoma at post-chemotherapy retroperitoneal lymph node dissection. The Indiana University testis cancer database was queried from 1988 to 2011 to identify all patients with primary testicular or retroperitoneal pure seminoma and who were found to have pure seminoma at post-chemotherapy retroperitoneal lymph node dissection. Clinical characteristics were reviewed and survival analysis was performed. A total of 36 patients met the study inclusion criteria. All patients received standard first line cisplatin based chemotherapy and 17 received salvage chemotherapy. The decision to proceed to retroperitoneal lymph node dissection was based on enlarging retroperitoneal mass and/or positron emission positivity in the majority of cases. Seven patients had undergone previous retroperitoneal lymph node dissection. Additional surgical procedures were required in 19 patients to achieve a complete resection. The 5-year cancer specific survival rate was 54%. However, only 9 of 36 patients remained continuously free of disease and of these patients 4 received adjuvant chemotherapy. Mean time from post-chemotherapy retroperitoneal lymph node dissection to death was 6.9 months. Second line chemotherapy, reoperative retroperitoneal lymph node dissection and earlier era of treatment were associated with poorer cancer specific survival. A total of 36 patients with pure seminoma were found to have viable pure seminoma at post-chemotherapy retroperitoneal lymph node dissection. While 5-year cancer specific survival was 54%, these surgeries are technically demanding and only a minority of patients achieves a durable cure from surgery alone. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights

  4. Better long-term survival in young patients with non-metastatic colorectal cancer after surgery, an analysis of 69,835 patients in SEER database.

    Directory of Open Access Journals (Sweden)

    Qingguo Li

    Full Text Available OBJECTIVE: To compare the long-term survival of colorectal cancer (CRC in young patients with elderly ones. METHODS: Using Surveillance, Epidemiology, and End Results (SEER population-based data, we identified 69,835 patients with non-metastatic colorectal cancer diagnosed between January 1, 1988 and December 31, 2003 treated with surgery. Patients were divided into young (40 years and under and elderly groups (over 40 years of age. Five-year cancer specific survival data were obtained. Kaplan-Meier methods were adopted and multivariable Cox regression models were built for the analysis of long-term survival outcomes and risk factors. RESULTS: Young patients showed significantly higher pathological grading (p<0.001, more cases of mucinous and signet-ring histological type (p<0.001, later AJCC stage (p<0.001, more lymph nodes (≥ 12 nodes dissected (p<0.001 and higher metastatic lymph node ratio (p<0.001. The 5-year colorectal cancer specific survival rates were 78.6% in young group and 75.3% in elderly group, which had significant difference in both univariate and multivariate analysis (P<0.001. Further analysis showed this significant difference only existed in stage II and III patients. CONCLUSIONS: Compared with elderly patients, young patients with colorectal cancer treated with surgery appear to have unique characteristics and a higher cancer specific survival rate although they presented with higher proportions of unfavorable biological behavior as well as advanced stage disease.

  5. Survival analysis using primary care electronic health record data: A systematic review of the literature.

    Science.gov (United States)

    Hodgkins, Adam Jose; Bonney, Andrew; Mullan, Judy; Mayne, Darren John; Barnett, Stephen

    2017-01-01

    An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority ( n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half ( n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

  6. Resveratrol improves survival, hemodynamics and energetics in a rat model of hypertension leading to heart failure.

    Science.gov (United States)

    Rimbaud, Stéphanie; Ruiz, Matthieu; Piquereau, Jérôme; Mateo, Philippe; Fortin, Dominique; Veksler, Vladimir; Garnier, Anne; Ventura-Clapier, Renée

    2011-01-01

    Heart failure (HF) is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT). Resveratrol (18 mg/kg/day; HS-RSV) was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition). Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, phypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α) expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium-dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF.

  7. Resveratrol improves survival, hemodynamics and energetics in a rat model of hypertension leading to heart failure.

    Directory of Open Access Journals (Sweden)

    Stéphanie Rimbaud

    Full Text Available Heart failure (HF is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT. Resveratrol (18 mg/kg/day; HS-RSV was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition. Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, p<0.001, and prevented the 25% reduction in body weight in HS-NT (P<0.001. Moreover, RSV counteracted the development of cardiac dysfunction (fractional shortening -34% in HS-NT as evaluated by echocardiography, which occurred without regression of hypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium-dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF.

  8. Resveratrol Improves Survival, Hemodynamics and Energetics in a Rat Model of Hypertension Leading to Heart Failure

    Science.gov (United States)

    Rimbaud, Stéphanie; Ruiz, Matthieu; Piquereau, Jérôme; Mateo, Philippe; Fortin, Dominique; Veksler, Vladimir; Garnier, Anne; Ventura-Clapier, Renée

    2011-01-01

    Heart failure (HF) is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT). Resveratrol (18 mg/kg/day; HS-RSV) was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition). Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, p<0.001), and prevented the 25% reduction in body weight in HS-NT (P<0.001). Moreover, RSV counteracted the development of cardiac dysfunction (fractional shortening −34% in HS-NT) as evaluated by echocardiography, which occurred without regression of hypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α) expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium–dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF. PMID:22028869

  9. Development of a likelihood of survival scoring system for hospitalized equine neonates using generalized boosted regression modeling.

    Directory of Open Access Journals (Sweden)

    Katarzyna A Dembek

    Full Text Available BACKGROUND: Medical management of critically ill equine neonates (foals can be expensive and labor intensive. Predicting the odds of foal survival using clinical information could facilitate the decision-making process for owners and clinicians. Numerous prognostic indicators and mathematical models to predict outcome in foals have been published; however, a validated scoring method to predict survival in sick foals has not been reported. The goal of this study was to develop and validate a scoring system that can be used by clinicians to predict likelihood of survival of equine neonates based on clinical data obtained on admission. METHODS AND RESULTS: Data from 339 hospitalized foals of less than four days of age admitted to three equine hospitals were included to develop the model. Thirty seven variables including historical information, physical examination and laboratory findings were analyzed by generalized boosted regression modeling (GBM to determine which ones would be included in the survival score. Of these, six variables were retained in the final model. The weight for each variable was calculated using a generalized linear model and the probability of survival for each total score was determined. The highest (7 and the lowest (0 scores represented 97% and 3% probability of survival, respectively. Accuracy of this survival score was validated in a prospective study on data from 283 hospitalized foals from the same three hospitals. Sensitivity, specificity, positive and negative predictive values for the survival score in the prospective population were 96%, 71%, 91%, and 85%, respectively. CONCLUSIONS: The survival score developed in our study was validated in a large number of foals with a wide range of diseases and can be easily implemented using data available in most equine hospitals. GBM was a useful tool to develop the survival score. Further evaluations of this scoring system in field conditions are needed.

  10. Demographic and Socio-economic Determinants of Birth Interval Dynamics in Manipur: A Survival Analysis

    Directory of Open Access Journals (Sweden)

    Sanajaoba Singh N,

    2011-01-01

    Full Text Available The birth interval is a major determinant of levels of fertility in high fertility populations. A house-to-house survey of 1225 women in Manipur, a tiny state in North Eastern India was carried out to investigate birth interval patterns and its determinants. Using survival analysis, among the nine explanatory variables of interest, only three factors – infant mortality, Lactation and use of contraceptive devices have highly significant effect (P<0.01 on the duration of birth interval and only three factors – age at marriage of wife, parity and sex of child are found to be significant (P<0.05 on the duration variable.

  11. Multivariable model development and internal validation for prostate cancer specific survival and overall survival after whole-gland salvage Iodine-125 prostate brachytherapy.

    Science.gov (United States)

    Peters, Max; van der Voort van Zyp, Jochem R N; Moerland, Marinus A; Hoekstra, Carel J; van de Pol, Sandrine; Westendorp, Hendrik; Maenhout, Metha; Kattevilder, Rob; Verkooijen, Helena M; van Rossum, Peter S N; Ahmed, Hashim U; Shah, Taimur T; Emberton, Mark; van Vulpen, Marco

    2016-04-01

    Whole-gland salvage Iodine-125-brachytherapy is a potentially curative treatment strategy for localised prostate cancer (PCa) recurrences after radiotherapy. Prognostic factors influencing PCa-specific and overall survival (PCaSS & OS) are not known. The objective of this study was to develop a multivariable, internally validated prognostic model for survival after whole-gland salvage I-125-brachytherapy. Whole-gland salvage I-125-brachytherapy patients treated in the Netherlands from 1993-2010 were included. Eligible patients had a transrectal ultrasound-guided biopsy-confirmed localised recurrence after biochemical failure (clinical judgement, ASTRO or Phoenix-definition). Recurrences were assessed clinically and with CT and/or MRI. Metastases were excluded using CT/MRI and technetium-99m scintigraphy. Multivariable Cox-regression was used to assess the predictive value of clinical characteristics in relation to PCa-specific and overall mortality. PCa-specific mortality was defined as patients dying with distant metastases present. Missing data were handled using multiple imputation (20 imputed sets). Internal validation was performed and the C-statistic calculated. Calibration plots were created to visually assess the goodness-of-fit of the final model. Optimism-corrected survival proportions were calculated. All analyses were performed according to the TRIPOD statement. Median total follow-up was 78months (range 5-139). A total of 62 patients were treated, of which 28 (45%) died from PCa after mean (±SD) 82 (±36) months. Overall, 36 patients (58%) patients died after mean 84 (±40) months. PSA doubling time (PSADT) remained a predictive factor for both types of mortality (PCa-specific and overall): corrected hazard ratio's (HR's) 0.92 (95% CI: 0.86-0.98, p=0.02) and 0.94 (95% CI: 0.90-0.99, p=0.01), respectively (C-statistics 0.71 and 0.69, respectively). Calibration was accurate up to 96month follow-up. Over 80% of patients can survive 8years if PSADT>24

  12. Impact of human immunodeficiency virus on survival after liver transplantation: analysis of United Network for Organ Sharing database.

    Science.gov (United States)

    Mindikoglu, Ayse L; Regev, Arie; Magder, Laurence S

    2008-02-15

    The outcome of liver transplantation (LT) in patients infected with human immunodeficiency virus (HIV) has been a matter of controversy. A retrospective cohort study was performed to assess the impact of HIV on LT survival by using United Network for Organ Sharing registry Standard Transplant Analysis and Research files. A total of 138 HIV(+) and 30,520 HIV(-) patients who were > or =18 years old and underwent LT during the highly active antiretroviral therapy era (starting January 1, 1997) in the United States were included. Among all HIV(+) patients, the estimated 2-year survival probability was lower (70%) than among non-HIV patients (81%). This excess risk appeared entirely among those with coinfections, that is, HIV with hepatitis B virus or hepatitis C virus (HCV), as none of the 24 HIV-infected patients who did not have hepatitis B virus or HCV died during an average of 1.2 years of follow-up per person. Among HCV(+) patients, those with HIV coinfection had significantly lower survival rates than patients without HIV (P=0.006). Controlling for age, coinfection, Model for End-Stage Liver Disease scores, and other potential confounders in a proportional hazards regression analysis, HIV(+) patients had a hazard ratio of 1.41 (P=0.14, 95% confidence interval: 0.90-2.22) for mortality after LT. HIV(+) patients without HCV coinfection seemed to have good prognosis, whereas patients who had HIV/HCV coinfection had poor outcomes, which were significantly worse than that seen in those with HCV alone.

  13. Snow model analysis.

    Science.gov (United States)

    2014-01-01

    This study developed a new snow model and a database which warehouses geometric, weather and traffic : data on New Jersey highways. The complexity of the model development lies in considering variable road : width, different spreading/plowing pattern...

  14. Human Engineered Heart Muscles Engraft and Survive Long-Term in a Rodent Myocardial Infarction Model

    Science.gov (United States)

    Riegler, Johannes; Tiburcy, Malte; Ebert, Antje; Tzatzalos, Evangeline; Raaz, Uwe; Abilez, Oscar J.; Shen, Qi; Kooreman, Nigel G.; Neofytou, Evgenios; Chen, Vincent C.; Wang, Mouer; Meyer, Tim; Tsao, Philip S.; Connolly, Andrew J.; Couture, Larry A.; Gold, Joseph D.; Zimmermann, Wolfram H.; Wu, Joseph C.

    2015-01-01

    Rational Tissue engineering approaches may improve survival and functional benefits from human embryonic stem cell-derived cardiomyocte (ESC-CM) transplantation, thereby potentially preventing dilative remodelling and progression to heart failure. Objective Assessment of transport stability, long term survival, structural organisation, functional benefits, and teratoma risk of engineered heart muscle (EHM) in a chronic myocardial infarction (MI) model. Methods and Results We constructed EHMs from ESC-CMs and released them for transatlantic shipping following predefined quality control criteria. Two days of shipment did not lead to adverse effects on cell viability or contractile performance of EHMs (n=3, P=0.83, P=0.87). After ischemia/reperfusion (I/R) injury, EHMs were implanted onto immunocompromised rat hearts at 1 month to simulate chronic ischemia. Bioluminescence imaging (BLI) showed stable engraftment with no significant cell loss between week 2 and 12 (n=6, P=0.67), preserving up to 25% of the transplanted cells. Despite high engraftment rates and attenuated disease progression (change in ejection fraction for EHMs −6.7±1.4% vs control −10.9±1.5%, n>12, P=0.05), we observed no difference between EHMs containing viable or non-viable human cardiomyocytes in this chronic xenotransplantation model (n>12, P=0.41). Grafted cardiomyocytes showed enhanced sarcomere alignment and increased connexin 43 expression at 220 days after transplantation. No teratomas or tumors were found in any of the animals (n=14) used for long-term monitoring. Conclusions EHM transplantation led to high engraftment rates, long term survival, and progressive maturation of human cardiomyocytes. However, cell engraftment was not correlated with functional improvements in this chronic MI model. Most importantly, the safety of this approach was demonstrated by the lack of tumor or teratoma formation. PMID:26291556

  15. A comparative study of two food model systems to test the survival of Campylobacter jejuni at -18 degrees C

    DEFF Research Database (Denmark)

    Birk, Tina; Rosenquist, Hanne; Brondsted, L.

    2006-01-01

    The survival of Campylobacter jejuni NCTC 11168 was tested at freezing conditions (-18 degrees C) over a period of 32 days in two food models that simulated either (i) the chicken skin surface (skin model) or (ii) the chicken juice in and around a broiler carcass (liquid model). In the skin model...

  16. What happens after discharge? An analysis of long-term survival in cardiac surgical patients requiring prolonged intensive care.

    Science.gov (United States)

    Elfstrom, K Miriam; Hatefi, Dustin; Kilgo, Patrick D; Puskas, John D; Thourani, Vinod H; Guyton, Robert A; Halkos, Michael E

    2012-01-01

    Cardiac surgical patients with postoperative complications frequently require prolonged intensive care yet survive to hospital discharge. From January 1, 2002 to December 31, 2007, 11,541 consecutive patients underwent cardiac operations at a single academic institution. Of these, 11,084 (95.9%) survived to hospital discharge and comprised the study sample. Patients were retrospectively categorized into four groups according to intensive care unit (ICU) length of stay (LOS): 14 days. Survival at 12 months was determined using the Social Security Death Index. Kaplan-Meier (KM) survival curves and Cox proportional hazards regression modeling (hazard ratio, HR) were used to analyze group differences in survival. One-year survival among the four groups according to ICU LOS was: 14 days, 68.3% (265/388) (p 14 days (HR = 1.90) compared to patients with ICU LOS 14 days (HR = 1.63). Although cardiac surgery patients with major postoperative complications frequently survive to hospital discharge, survival after discharge is significantly reduced in patients requiring prolonged ICU care. Reduced survival in patients with a high risk of complications and anticipated long ICU stays should be considered when discussing surgical versus nonsurgical options. © 2011 Wiley Periodicals, Inc.

  17. Acinetobacter spp. are associated with a higher mortality in intensive care patients with bacteremia: a survival analysis.

    Science.gov (United States)

    Leão, Aline C Q; Menezes, Paulo R; Oliveira, Maura S; Levin, Anna S

    2016-08-09

    It has been challenging to determine the true clinical impact of Acinetobacter spp., due to the predilection of this pathogen to colonize and infect critically ill patients, who often have a poor prognosis. The aim of this study was to assess whether Acinetobacter spp. bacteremia is associated with lower survival compared with bacteremia caused by other pathogens in critically ill patients. This study was performed at Hospital das Clínicas, University of São Paulo, Brazil. There are 12 intensive care units (ICUs) in the hospital: five Internal Medicine ICUs (emergency, nephrology, infectious diseases and respiratory critical care), three surgical ICU (for general surgery and liver transplantion), an Emergency Department ICU for trauma patients, an ICU for burned patients, a neurosurgical ICU and a post-operative ICU. A retrospective review of medical records was conducted for all patients admitted to any of the ICUs, who developed bacteremia from January 2010 through December 2011. Patients with Acinetobacter spp. were compared with those with other pathogens (Klebsiella pneumoniae, Staphylococcus aureus, Enterobacter spp., Enterococcus spp., Pseudomonas aeruginosa). We did a 30-day survival analysis. The Kaplan-Meier method and log-rank test were used to determine the overall survival. Potential prognostic factors were identified by bivariate and multivariate Cox regression analysis. One hundred forty-one patients were evaluated. No differences between patients with Acinetobacter spp. and other pathogens were observed with regard to age, sex, APACHE II score, Charlson Comorbidity Score and type of infection. Initial inappropriate antimicrobial treatment was more frequent in Acinetobacter bacteremia (88 % vs 51 %). Bivariate analysis showed that age > 60 years, diabetes mellitus, and Acinetobacter spp. infection were significantly associated with a poor prognosis. Multivariate model showed that Acinetobacter spp. infection (HR = 1.93, 95 % CI: 1

  18. Analysis of feedbacks between nucleation rate, survival probability and cloud condensation nuclei formation

    Science.gov (United States)

    Westervelt, D. M.; Pierce, J. R.; Adams, P. J.

    2014-06-01

    Aerosol nucleation is an important source of particle number in the atmosphere. However, in order to become cloud condensation nuclei (CCN), freshly nucleated particles must undergo significant condensational growth while avoiding coagulational scavenging. In an effort to quantify the contribution of nucleation to CCN, this work uses the GEOS-Chem-TOMAS global aerosol model to calculate changes in CCN concentrations against a broad range of nucleation rates and mechanisms. We then quantify the factors that control CCN formation from nucleation, including daily nucleation rates, growth rates, coagulation sinks, condensation sinks, survival probabilities, and CCN formation rates, in order to examine feedbacks that may limit growth of nucleated particles to CCN. Nucleation rate parameterizations tested in GEOS-Chem-TOMAS include ternary nucleation (with multiple tuning factors), activation nucleation (with two pre-factors), binary nucleation, and ion-mediated nucleation. We find that nucleation makes a significant contribution to boundary layer CCN(0.2%), but this contribution is only modestly sensitive to the choice of nucleation scheme, ranging from 49 to 78% increase in concentrations over a control simulation with no nucleation. Moreover, a two order-of-magnitude increase in the globally averaged nucleation rate (via changes to tuning factors) results in small changes (less than 10%) to global CCN(0.2%) concentrations. To explain this, we present a simple theory showing that survival probability has an exponentially decreasing dependence on the square of the condensation sink. This functional form stems from a negative correlation between condensation sink and growth rate and a positive correlation between condensation sink and coagulational scavenging. Conceptually, with a fixed condensable vapor budget (sulfuric acid and organics), any increase in CCN concentrations due to higher nucleation rates necessarily entails an increased aerosol surface area in the

  19. Pretransplant prediction of posttransplant survival for liver recipients with benign end-stage liver diseases: a nonlinear model.

    Directory of Open Access Journals (Sweden)

    Ming Zhang

    Full Text Available BACKGROUND: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients' survival with benign end-stage liver diseases (BESLD by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD and a general-illness severity model (the sequential organ failure assessment score, or SOFA score. METHODOLOGY/PRINCIPAL FINDINGS: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na(+; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001 in one-year and 0.88 (P<0.001 in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction. CONCLUSIONS/SIGNIFICANCE: The posttransplant prognosis is a multidimensional nonlinear

  20. Model Checking as Static Analysis

    DEFF Research Database (Denmark)

    Zhang, Fuyuan

    to a multi-valued setting, and we therefore obtain a multivalued analysis for temporal properties specied by CTL formulas. In particular, we have shown that the three-valued CTL model checking problem over Kripke modal transition systems can be exactly encoded in three-valued ALFP. Last, we come back to two-valued......Both model checking and static analysis are prominent approaches to detecting software errors. Model Checking is a successful formal method for verifying properties specified in temporal logics with respect to transition systems. Static analysis is also a powerful method for validating program...... properties which can predict safe approximations to program behaviors. In this thesis, we have developed several static analysis based techniques to solve model checking problems, aiming at showing the link between static analysis and model checking. We focus on logical approaches to static analysis...

  1. Time trend analysis of primary tumor resection for stage IV colorectal cancer: less surgery, improved survival.

    Science.gov (United States)

    Hu, Chung-Yuan; Bailey, Christina E; You, Y Nancy; Skibber, John M; Rodriguez-Bigas, Miguel A; Feig, Barry W; Chang, George J

    2015-03-01

    With the advent of effective modern chemotherapeutic and biologic agents, primary tumor resection for patients with stage IV colorectal cancer (CRC) may not be routinely necessary. To evaluate the secular patterns of primary tumor resection use in stage IV CRC in the United States. A retrospective cohort study using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results CRC registry. Demographic and clinical factors were compared for 64,157 patients diagnosed with stage IV colon or rectal cancer from January 1, 1988, through December 31, 2010, who had undergone primary tumor resection and those who had not. Rates of primary tumor resection and median relative survival were calculated for each year. Joinpoint regression analysis was used to determine when a significant change in trend in the primary tumor resection rate had occurred. Logistic regression analysis was used to assess factors associated with primary tumor resection. Difference in primary tumor resection rates over time. Of the 64,157 patients with stage IV CRC, 43,273 (67.4%) had undergone primary tumor resection. The annual rate of primary tumor resection decreased from 74.5% in 1988 to 57.4% in 2010 (Ptrend toward fewer primary tumor resections was seen. Despite the decreasing primary tumor resection rate, patient survival rates improved. However, primary tumor resection may still be overused, and current treatment practices lag behind evidence-based treatment guidelines.

  2. Assessment of survival of patients with metastatic clear cell renal cell carcinoma after radical cytoreductive nephrectomy versus no surgery: a SEER analysis

    Directory of Open Access Journals (Sweden)

    Wen-Jun Xiao

    2015-04-01

    Full Text Available Purposes To examine the factors related to the choice of cytoreductive nephrectomy (CN for patients with metastatic clear cell renal cell carcinoma (mCCRCC, and compare the population-based survival rates of patients treated with or without surgery in the modern targeted therapy era. Materials and Methods From 2006 to 2009, patients with mCCRCC were identified from SEER database. The factors that affected patients to be submitted to CN were examined and propensity scores for each patient were calculated. Then patients were matched based upon propensity scores. Univariable and multivariable cox regression models were used to compare survival rates of patients treated with or without surgery. Finally, sensitivity analysis for the cox model on a hazard ratio scale was performed. Results Age, race, tumor size, T stage and N stage were associated with nephrectomy univariablely. After the match based upon propensity scores, the 1-, 2-, and 3-year cancer-specific survival rate estimates were 45.1%, 27.9%, and 21.7% for the no-surgery group vs 70.6%, 52.2%, and 41.7% for the surgery group, respectively (hazard ratio 0.42, 95%CI: 0.35-0.52, log-rank P<0.001. In multivariable Cox proportional hazard regression model, race, T stage, N stage and median household income were significantly associated with survival. Sensitivity analysis on a hazard ratio scale indicated that the hazard ratio might be above 1.00 only when the unknown factor had an opposite effect on survival which was 3-fold than CN. Conclusion The results of our study showed that CN significantly improves the survival of patients with metastatic CCRCC even in the targeted therapy era.

  3. A mixed linear model controlling for case underascertainment across multiple cancer registries estimated time trends in survival.

    Science.gov (United States)

    Dahm, Stefan; Bertz, Joachim; Barnes, Benjamin; Kraywinkel, Klaus

    2018-01-10

    Large temporal and geographical variation in survival rates estimated from epidemiological cancer registries coupled with heterogeneity in death certificate only (DCO) notifications makes it difficult to interpret trends in survival. The aim of our study is to introduce a method for estimating such trends while accounting for heterogeneity in DCO notifications in a cancer site-specific manner. We used the data of 4.0 million cancer cases notified in 14 German epidemiological cancer registries. Annual 5-year relative survival rates from 2002 through 2013 were estimated, and proportions of DCO notifications were recorded. "DCO-excluded" survival rates were regressed on DCO proportions and calendar years using a mixed linear model with cancer registry as a random effect. Based on this model, trends in survival rates were estimated for Germany at 0% DCO. For most cancer sites and age groups, we estimated significant positive trends in survival. Age-standardized survival for all cancers combined increased by 7.1% units for women and 10.8% units for men. The described method could be used to estimate trends in cancer survival based on the data from epidemiological cancer registries with differing DCO proportions and with changing DCO proportions over time. Copyright © 2018 Elsevier Inc. All rights reserved.

  4. Comparison of methods for estimating the attributable risk in the context of survival analysis

    Directory of Open Access Journals (Sweden)

    Malamine Gassama

    2017-01-01

    Full Text Available Abstract Background The attributable risk (AR measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Methods Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier’s estimator, one semiparametric based on Cox’s model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox’s model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points. Results Under proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort. Conclusion In practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of

  5. Comparison of methods for estimating the attributable risk in the context of survival analysis.

    Science.gov (United States)

    Gassama, Malamine; Bénichou, Jacques; Dartois, Laureen; Thiébaut, Anne C M

    2017-01-23

    The attributable risk (AR) measures the proportion of disease cases that can be attributed to an exposure in the population. Several definitions and estimation methods have been proposed for survival data. Using simulations, we compared four methods for estimating AR defined in terms of survival functions: two nonparametric methods based on Kaplan-Meier's estimator, one semiparametric based on Cox's model, and one parametric based on the piecewise constant hazards model, as well as one simpler method based on estimated exposure prevalence at baseline and Cox's model hazard ratio. We considered a fixed binary exposure with varying exposure probabilities and strengths of association, and generated event times from a proportional hazards model with constant or monotonic (decreasing or increasing) Weibull baseline hazard, as well as from a nonproportional hazards model. We simulated 1,000 independent samples of size 1,000 or 10,000. The methods were compared in terms of mean bias, mean estimated standard error, empirical standard deviation and 95% confidence interval coverage probability at four equally spaced time points. Under proportional hazards, all five methods yielded unbiased results regardless of sample size. Nonparametric methods displayed greater variability than other approaches. All methods showed satisfactory coverage except for nonparametric methods at the end of follow-up for a sample size of 1,000 especially. With nonproportional hazards, nonparametric methods yielded similar results to those under proportional hazards, whereas semiparametric and parametric approaches that both relied on the proportional hazards assumption performed poorly. These methods were applied to estimate the AR of breast cancer due to menopausal hormone therapy in 38,359 women of the E3N cohort. In practice, our study suggests to use the semiparametric or parametric approaches to estimate AR as a function of time in cohort studies if the proportional hazards assumption appears

  6. Inhibition of the Mitochondrial Fission Protein Drp1 Improves Survival in a Murine Cardiac Arrest Model

    Science.gov (United States)

    Sharp, Willard W.; Beiser, David G.; Fang, Yong Hu; Han, Mei; Piao, Lin; Varughese, Justin; Archer, Stephen L.

    2015-01-01

    Objectives Survival following sudden cardiac arrest is poor despite advances in cardiopulmonary resuscitation (CPR) and the use of therapeutic hypothermia. Dynamin related protein 1 (Drp1), a regulator of mitochondrial fission, is an important determinant of reactive oxygen species generation, myocardial necrosis, and left ventricular function following ischemia/reperfusion injury, but its role in cardiac arrest is unknown. We hypothesized that Drp1 inhibition would improve survival, cardiac hemodynamics, and mitochondrial function in an in vivo model of cardiac arrest. Design Laboratory investigation. Setting University laboratory Interventions Anesthetized and ventilated adult female C57BL/6 wild-type mice underwent an 8-min KCl induced cardiac arrest followed by 90 seconds of CPR. Mice were then blindly randomized to a single intravenous injection of Mdivi-1 (0.24 mg/kg), a small molecule Drp1 inhibitor or vehicle (DMSO). Measurements and Main Results Following resuscitation from cardiac arrest, mitochondrial fission was evidenced by Drp1 translocation to the mitochondrial membrane and a decrease in mitochondrial size. Mitochondrial fission was associated with increased lactate and evidence of oxidative damage. Mdivi-1 administration during CPR inhibited Drp1 activation, preserved mitochondrial morphology, and decreased oxidative damage. Mdivi-1 also reduced the time to return of spontaneous circulation (ROSC) 116±4 vs. 143±7 sec (pcardiac arrest. Conclusions Post cardiac arrest inhibition of Drp1 improves time to ROSC and myocardial hemodynamics resulting in improved survival and neurological outcomes in a murine model of cardiac arrest. Pharmacological targeting of mitochondrial fission may be a promising therapy for cardiac arrest. PMID:25599491

  7. Plasma Resuscitation Improved Survival in a Cecal Ligation and Puncture Rat Model of Sepsis.

    Science.gov (United States)

    Chang, Ronald; Holcomb, John B; Johansson, Par I; Pati, Shibani; Schreiber, Martin A; Wade, Charles E

    2017-06-06

    The paradigm shift from crystalloid to plasma resuscitation of traumatic hemorrhagic shock has improved patient outcomes due in part to plasma-mediated reversal of catecholamine and inflammation-induced endothelial injury, decreasing vascular permeability and attenuating organ injury. Since sepsis induces a similar endothelial injury as seen in hemorrhage, we hypothesized that plasma resuscitation would increase 48-hour survival in a rat sepsis model. Adult male Sprague-Dawley rats (375-425 g) were subjected to 35% cecal ligation and puncture (CLP) (t = 0 h). Twenty-two hours post-CLP and prior to resuscitation (t = 22 h), animals were randomized to resuscitation with normal saline (NS, 10 cc/kg/hr) or pooled rat fresh frozen plasma (FFP, 3.33 cc/kg/hr). Resuscitation under general anesthesia proceeded for the next six hours (t = 22 h to t = 28 h); lactate was checked every 2 hours, and fluid volumes were titrated based on lactate clearance. Blood samples were obtained before (t = 22 h) and after resuscitation (t = 28 h), and at death or study conclusion. Lung specimens were obtained for calculation of wet-to-dry weight ratio. Fisher's exact test was used to analyze the primary outcome of 48-hour survival. ANOVA with repeated measures was used to analyze the effect of FFP versus NS resuscitation on blood gas, electrolytes, blood urea nitrogen (BUN), creatinine, interleukin (IL)-6, IL-10, catecholamines, and syndecan-1 (marker for endothelial injury). A two-tailed alpha level of dry weight ratio (5.28 vs 5.94) (all p < 0.05). Compared to crystalloid, plasma resuscitation increased 48-hour survival in a rat sepsis model, improved pulmonary function and decreased pulmonary edema, and attenuated markers for inflammation, endothelial injury, and catecholamines.

  8. Resin Versus Glass Microspheres for90Y Transarterial Radioembolization: Comparing Survival in Unresectable Hepatocellular Carcinoma Using Pretreatment Partition Model Dosimetry.

    Science.gov (United States)

    Van Der Gucht, Axel; Jreige, Mario; Denys, Alban; Blanc-Durand, Paul; Boubaker, Ariane; Pomoni, Anastasia; Mitsakis, Periklis; Silva-Monteiro, Marina; Gnesin, Silvano; Lalonde, Marie Nicod; Duran, Rafael; Prior, John O; Schaefer, Niklaus

    2017-08-01

    The aim of this study was to compare survival of patients treated for unresectable hepatocellular carcinoma (uHCC) with 90 Y transarterial radioembolization (TARE) using pretreatment partition model dosimetry (PMD). Methods: We performed a retrospective analysis of prospectively collected data on 77 patients consecutively treated (mean age ± SD, 66.4 ± 12.2 y) for uHCC (36 uninodular, 5 multinodular, 36 diffuse) with 90 Y TARE (41 resin, 36 glass) using pretreatment PMD. Study endpoints were progression-free survival (PFS) and overall survival (OS) assessed by Kaplan-Meier estimates. Several variables including Barcelona Clinic Liver Cancer (BCLC) staging system, tumor size, and serum α-fetoprotein (AFP) level were investigated using Cox proportional hazards regression. Results: The characteristics of 2 groups were comparable with regard to demographic data, comorbidities, Child-Pugh score, BCLC, serum AFP level, and 90 Y global administered activity. The median follow-up time was 7.7 mo (range, 0.4-50.1 mo). Relapse occurred in 44 patients (57%) at a median of 6 mo (range, 0.4-27.9 mo) after 90 Y TARE, and 41 patients (53%) died from tumor progression. Comparison between resin and glass microspheres revealed higher but not statistically significantly PFS and OS rates in the 90 Y resin group than the 90 Y glass group (resin PFS 6.1 mo [95% confidence interval CI, 4.7-7.4] and glass PFS 5 mo [95% CI, 0.9-9.2], P = 0.53; resin OS 7.7 mo [95% CI, 7.2-8.2] and glass OS 7 mo [95% CI 1.6-12.4], P = 0.77). No significant survival difference between both types of 90 Y microspheres was observed in any subgroups of patients with early/intermediate or advanced BCLC stages. Among the variables investigated, Cox analyses showed that only in the glass group, the BCLC staging system and the serum AFP level were associated with PFS ( P = 0.04) and OS ( P = 0.04). Tumor size was a prognostic factor without significant influence on PFS and OS after 90 Y TARE. Conclusion

  9. Creation of a Prognostic Index for Spine Metastasis to Stratify Survival in Patients Treated With Spinal Stereotactic Radiosurgery: Secondary Analysis of Mature Prospective Trials

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Chad [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Hess, Kenneth [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Bishop, Andrew J.; Pan, Hubert Y.; Christensen, Eva N. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Yang, James N. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Tannir, Nizar [Department of Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Amini, Behrang [Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Tatsui, Claudio; Rhines, Laurence [Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Brown, Paul [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Ghia, Amol, E-mail: ajghia@mdanderson.org [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

    2015-09-01

    Purpose: There exists uncertainty in the prognosis of patients following spinal metastasis treatment. We sought to create a scoring system that stratifies patients based on overall survival. Methods and Materials: Patients enrolled in 2 prospective trials investigating stereotactic spine radiation surgery (SSRS) for spinal metastasis with ≥3-year follow-up were analyzed. A multivariate Cox regression model was used to create a survival model. Pretreatment variables included were race, sex, age, performance status, tumor histology, extent of vertebrae involvement, previous therapy at the SSRS site, disease burden, and timing of diagnosis and metastasis. Four survival groups were generated based on the model-derived survival score. Results: Median follow-up in the 206 patients included in this analysis was 70 months (range: 37-133 months). Seven variables were selected: female sex (hazard ratio [HR] = 0.7, P=.02), Karnofsky performance score (HR = 0.8 per 10-point increase above 60, P=.007), previous surgery at the SSRS site (HR = 0.7, P=.02), previous radiation at the SSRS site (HR = 1.8, P=.001), the SSRS site as the only site of metastatic disease (HR = 0.5, P=.01), number of organ systems involved outside of bone (HR = 1.4 per involved system, P<.001), and >5 year interval from initial diagnosis to detection of spine metastasis (HR = 0.5, P<.001). The median survival among all patients was 25.5 months and was significantly different among survival groups (in group 1 [excellent prognosis], median survival was not reached; group 2 reached 32.4 months; group 3 reached 22.2 months; and group 4 [poor prognosis] reached 9.1 months; P<.001). Pretreatment symptom burden was significantly higher in the patient group with poor survival than in the group with excellent survival (all metrics, P<.05). Conclusions: We developed the prognostic index for spinal metastases (PRISM) model, a new model that identified patient subgroups with poor and excellent prognoses.

  10. Surviving in a Cosexual World: A Cost-Benefit Analysis of Dioecy in Tropical Trees.

    Science.gov (United States)

    Bruijning, Marjolein; Visser, Marco D; Muller-Landau, Helene C; Wright, S Joseph; Comita, Liza S; Hubbell, Stephen P; de Kroon, Hans; Jongejans, Eelke

    2017-03-01

    Dioecy has a demographic disadvantage compared with hermaphroditism: only about half of reproductive adults produce seeds. Dioecious species must therefore have fitness advantages to compensate for this cost through increased survival, growth, and/or reproduction. We used a full life cycle approach to quantify the demographic costs and benefits associated with dioecy while controlling for demographic differences between dioecious and hermaphroditic species related to other functional traits. The advantage of this novel approach is that we can focus on the effect of breeding system across a diverse tree community. We built a composite integral projection model for hermaphroditic and dioecious tree populations from Barro Colorado Island, Panama, using long-term demographic and newly collected reproductive data. Integration of all costs and benefits showed that compensation was realized through increased seed production, resulting in no net costs of dioecy. Compensation was also facilitated by the low contribution of reproduction to population growth. Estimated positive effects of dioecy on tree growth and survival were small and insignificant for population growth rates. Our model revealed that, for long-lived organisms, the cost of having males is smaller than generally expected. Hence, little compensation is required for dioecious species to maintain population growth rates similar to those of hermaphroditic species.

  11. Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

    Science.gov (United States)

    Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

    2017-06-01

    We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Primary myelofibrosis: a detailed statistical analysis of the clinicopathological variables influencing survival.

    Science.gov (United States)

    Rupoli, S; Da Lio, L; Sisti, S; Campanati, G; Salvi, A; Brianzoni, M F; D'Amico, S; Cinciripini, A; Leoni, P

    1994-04-01

    In the present study we analyzed the prognostic significance of several clinical, hematological, and histological parameters recorded at diagnosis in a consecutive series of 72 patients with primary myelofibrosis (PMF). Univariate analysis showed that the most significant indicators of poor survival were the following: age greater than 60, splenomegaly, anemia (hemoglobin > 10 g/dl), leukopenia (WBC 14 x 10(9)/l), and any of these histological features: adipose tissue and megakaryocyte reduction, prominent osteoblastic rims along the trabecular bone, presence of peritrabecular megakaryocytes (Mk), absence of normal or giant Mk. The multivariate analysis showed that only the level of hemoglobin and the presence of both normal Mk and fever independently influenced the prognosis. These parameters were used to set up a prognostic scoring system, allowing a feasible prognosis to be made for each patient at the time of diagnosis and identifying those patients in urgent need of new therapeutic approaches.

  13. Lichens as a model-system for survival of eukaryotic symbiotic associations to simulated space conditions

    Science.gov (United States)

    de Vera, J.-P.; Horneck, G.; Rettberg, P.; Ott, S.

    Lichens are symbiotic organisms associated by a fungus (mycobiont) and a a photosynthetic biont. As a consequence of the symbiotic state both the bionts are able to colonise habitats where the separate bionts would not be able to survive. The symbiosis of lichens reflects a high degree of complexity and plasticity. The combination of the different bionts enables these organisms to colonise most extreme habitats worldwide as polar regions, deserts and alpine zones. Besides the well investigated microorganisms lichens are good modelsystems to examine adaptation strategies to most extreme environmental conditions. They clearly demonstrate a high resistence to simulated space conditions concerning UV spectra (λ ≥ 160 nm) and vacuum (p = 10-5 Pa). Lichens are poikilohydric organisms. They are physiologically active if they are wet but if dry they are in the state of anabiosis. Lichens are highly resistant to simulated space conditions if they are in the dry state as has been examined in the lichen and the respective bionts of Xanthoria elegans. We performed experiments to test the resistence of wet lichens while they are physiologically active for comparison. Buellia frigida from sites on the Antarctic continent and Peltigera aphthosa colonising shady habitats in Norway have been used. The influence of different doses of UV-C on the viability of both lichen species has been studied. The analysis of the results has been done by Confocal Laser Scanning Microscopy (CLSM) using fluorescent LIVE/DEAD-substances. While B. frigida shows a very high resistence combined with a high viability to UV-C during the whole experiment the viability of the shady lichen P. aphthosadecreases immediately. The results clearly show a graded resistence in the lichen symbiosis depending on the adaptation mechanisms to the respective environmental conditions. These results will be discussed and compared with results achieved in former investigations with lichen species. The adaptation

  14. Bisphosphonates in the adjuvant setting of breast cancer therapy--effect on survival: a systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Irit Ben-Aharon

    Full Text Available The role of bisphosphonates (BP in early breast cancer (BC has been considered controversial. We performed a meta-analysis of all randomized controlled trials (RCTs that appraised the effects of BP on survival in early BC.RCTs were identified by searching the Cochrane Library, MEDLINE databases and conference proceedings. Hazard ratios (HRs of overall survival (OS, disease-free survival (DFS and relative risks of adverse events were estimated and pooled.Thirteen trials met the inclusion criteria, evaluating a total of 15,762 patients. Meta-analysis of ten trials which reported OS revealed no statistically significant benefit in OS for BP (HR 0.89, 95% CI = 0.79 to 1.01. Meta-analysis of nine trials which reported the DFS revealed no benefit in DFS (HR 0.95 (0.81-1.12. Meta-analysis upon menopausal status showed a statistically significant better DFS in the BP-treated patients (HR 0.81(0.69-0.95. In meta-regression, chemotherapy was negatively associated with HR of survival.Our meta-analysis indicates a positive effect for adjuvant BP on survival only in postmenopausal patients. Meta-regression demonstrated a negative association between chemotherapy use BP effect on survival. Further large scale RCTs are warranted to unravel the specific subgroups that would benefit from the addition of BP in the adjuvant setting.

  15. NanOx, a new model to predict cell survival in the context of particle therapy

    Science.gov (United States)

    Cunha, M.; Monini, C.; Testa, E.; Beuve, M.

    2017-02-01

    Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in

  16. A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival.

    Directory of Open Access Journals (Sweden)

    Branko Miladinovic

    Full Text Available Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R(2, scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R(2 =0.298; 95% CI: 0.236-0.358 than the Cox model (R(2 =0.156; 95% CI: 0.111-0.203. The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

  17. Modeling the effect of temperature on survival rate of Listeria monocytogenes in yogurt.

    Science.gov (United States)

    Szczawiński, J; Szczawińska, M E; Łobacz, A; Jackowska-Tracz, A

    2016-01-01

    The aim of the study was to (i) evaluate the behavior of Listeria monocytogenes in a commercially produced yogurt, (ii) determine the survival/inactivation rates of L. monocytogenes during cold storage of yogurt and (iii) to generate primary and secondary mathematical models to predict the behavior of these bacteria during storage at different temperatures. The samples of yogurt were inoculated with the mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12 and 15°C for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14 and 16 days of storage. From each sample a series of decimal dilutions were prepared and plated onto ALOA agar (agar for Listeria according to Ottaviani and Agosti). It was found that applied temperature and storage time significantly influenced the survival rate of listeriae (pyogurt stored under temperature range from 3 to 15°C, however, the polynomial model gave a better fit to the experimental data.

  18. The Antarctic nematode Plectus murrayi: an emerging model to study multiple stress survival.

    Science.gov (United States)

    Adhikari, Bishwo N; Tomasel, Cecilia M; Li, Grace; Wall, Diana H; Adams, Byron J

    2010-11-01

    The genus Plectus is one of the most widely distributed and common nematode taxa of freshwater and terrestrial habitats in the world, and is of particular interest because of its phylogenetic position relative to the origin of the Secernentean radiation. Plectus murrayi, a bacteria-feeding nematode, inhabits both semi-aquatic and terrestrial biotopes in the Antarctic McMurdo Dry Valleys (MCM), where its distribution is limited by organic carbon and soil moisture. Plectus nematodes from the MCM can survive extreme desiccation, freezing conditions, and other types of stress. Ongoing investigations of the physiological and molecular aspects of the stress biology of P. murrayi, along with the availability of genomic resources, will likely establish this nematode as an excellent invertebrate model system for studies of extreme environmental survival, and may provide a valuable source of genomic resources for comparative studies in other organisms. Moreover, because P. murrayi and Caenorhabditis elegans share a most recent common ancestor with the rest of the Secernentea, and given the ability of P. murrayi to be cultured at lower temperatures compared to C. elegans, P. murrayi could also be an emerging model system for the study of the evolution of environment-sensitive (stress response) alleles in nematodes.

  19. Hydroxocobalamin and epinephrine both improve survival in a swine model of cyanide-induced cardiac arrest.

    Science.gov (United States)

    Bebarta, Vikhyat S; Pitotti, Rebecca L; Dixon, Patricia S; Valtier, Sandra; Esquivel, Luis; Bush, Anneke; Little, Charles M

    2012-10-01

    To determine whether hydroxocobalamin will improve survival compared with epinephrine and saline solution controls in a model of cyanide-induced cardiac arrest. Forty-five swine (38 to 42 kg) were tracheally intubated, anesthetized, and central venous and arterial continuous cardiovascular monitoring catheters were inserted. Potassium cyanide was infused until cardiac arrest developed, defined as mean arterial pressure less than 30 mm Hg. Animals were treated with standardized mechanical chest compressions and were randomly assigned to receive one of 3 intravenous bolus therapies: hydroxocobalamin, epinephrine, or saline solution (control). All animals were monitored for 60 minutes after cardiac arrest. Additional epinephrine infusions were used in all arms of the study after return of spontaneous circulation for systolic blood pressure less than 90 mm Hg. A sample size of 15 animals per group was determined according to a power of 80%, a survival difference of 0.5, and an α of 0.05. Repeated-measure ANOVA was used to determine statistically significant changes between groups over time. Baseline weight, time to arrest, and cyanide dose at cardiac arrest were similar in the 3 groups. Coronary perfusion pressures with chest compressions were greater than 15 mm Hg in both treatment groups indicating sufficient compression depth. Zero of 15 (95% confidence interval [CI] 0% to 25%) animals in the control group, 11 of 15 (73%; 95% CI 48% to 90%) in the hydroxocobalamin group, and 11 of 15 (73%; 95% CI 48% to 90%) in the epinephrine group survived to the conclusion of the study (Pcyanide levels in the hydroxocobalamin group were also lower than that of the epinephrine group from cardiac arrest through the conclusion of the study. Intravenous hydroxocobalamin and epinephrine both independently improved survival compared with saline solution control in our swine model of cyanide-induced cardiac arrest. Hydroxocobalamin improved mean arterial pressure and pH, decreased

  20. Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer

    Directory of Open Access Journals (Sweden)

    Yi Ren

    2013-01-01

    Full Text Available Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable (P<0.15 and multivariable models with forward selection (P<0.05 for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.

  1. Liver recurrence in endometrial cancer: a multi-institutional analysis of factors predictive of postrecurrence survival.

    Science.gov (United States)

    Toptas, Tayfun; Karalok, Alper; Ureyen, Isin; Tasci, Tolga; Erol, Onur; Bozkurt, Selen; Tulunay, Gokhan; Simsek, Tayup; Turan, Taner

    2016-10-01

    Predictive factors for survival following liver metastasis in endometrial cancer (EC) have not been studied to date. It is expected that patients who initially presented with liver metastasis or developed liver metastasis as the subsequent metastatic site of progressive disease are likely to have poor outcomes. However, patients developing liver metastasis as the first site of recurrence may have a chance of benefiting from the salvage therapies. Therefore, we aimed to determine factors influencing postrecurrence survival in EC patients who developed liver metastasis as the first site of recurrence. Patients with EC who underwent primary surgery at three centers between 1993 and 2013 were reviewed. Liver recurrence was defined as documentation of parenchymal liver metastasis either by radiologically or biopsy, after a disease-free interval of ≥3 months. Patients with liver metastasis at presentation, or liver metastasis as the subsequent metastatic site of progressive disease were excluded. Forty-six patients were identified. Median time to liver recurrence was 12 months, with 91.3 % of recurrences detected within 3 years. Most patients (73.9 %) had liver recurrence concomitant with extra-hepatic disease. Median survival after the diagnosis of liver recurrence was 9 months. While in univariate analysis, time to liver recurrence (p liver recurrence (p < 0.001) was the only independent predictor. This criterion may be used as a marker for stratifying patients into different prognostic risk groups and for selection of patients for salvage therapies.

  2. Tooth Loss and Survival Analysis after Traumatic Injuries in Primary Dentition

    Directory of Open Access Journals (Sweden)

    Galovic Jelena

    2017-11-01

    Full Text Available Background/Aim: The aim of the present study was to investigate the treatment options, survival rate of traumatized primary teeth and evaluate the factors influencing the outcome. Material and Methods: The sample consisted of all dental trauma cases treated over a 14 years period at the Department of Pediatric and Preventive Dentistry, Dental Clinic of Vojvodina, Novi Sad. Criteria for inclusion in this study were: dental trauma to primary teeth and age in the moment of injury up to seven years. Dental trauma records were analyzed in order to obtain the following: gender and age of the child at the time of trauma, type of trauma, as well as the type and timing of treatment received. After data analysis a survival rate of traumatized primary teeth was evaluated. Results: The study was designed as retrospective and it included 225 children, with 346 traumatized primary teeth. The occurrence of trauma was higher in male patients (60,4% and in children up to 4 years of age. Luxations were more frequent (72.8% compared to isolated teeth fractures (20.8%, while the two types of injury combined were rare (6,3%. One year following dental trauma 231 teeth (0.67% developed complications. Falls were the main cause of trauma (68.9% and the presence of more than one traumatized tooth was frequent. A percentage of 48.8 children received dental care during first 24 h after the injury. Conclusions: Survival of injured primary teeth is relatively low, regardless of trauma type, time interval between injury and treatment and the type of provided treatment.

  3. Survival analysis of a critical resource for cavity-nesting communities: patterns of tree cavity longevity.

    Science.gov (United States)

    Edworthy, Amanda B; Wiebe, Karen L; Martin, Kathy

    2012-09-01

    Tree cavities are a vital multi-annual resource used by cavity-nesting birds and mammals for nesting and shelter. The abundance of this resource will be influenced by the rates at which cavities are created and destroyed. We applied the demographic concepts of survival and longevity to populations of tree holes to investigate rates of loss for cavities in three tree species, as well as how characteristics of nest trees, habitat type, and species of excavator affected the persistence of tree cavities in trembling aspen, Populus tremuloides (95% of cavities were in aspen trees), in interior British Columbia, Canada. By modeling survival of 1635 nesting cavities in aspen over a time span of 16 years, we found that the decay stage of the nest tree was the most important factor determining cavity longevity. Cavities in trees with advanced decay had a relatively short median longevity of 7 years (95% CI 6-9 years), whereas those in living trees had a median longevity of more than 15 years. We found that cavity longevity was greater in continuous forest than in aspen grove habitat. Interestingly, cavities formed by weak excavators survived as long as those created by Northern Flickers (Colaptes auratus), despite occurring in more decayed tree stems. Thus, weak excavators may be selecting for characteristics that make a tree persistent, such as a broken top. Our results indicate that retention of cavities in large, live aspen trees is necessary to conserve persistent cavities, and that cavity longevity will have a large effect on the structure and function of cavity-using vertebrate communities.

  4. Review and evaluation of performance measures for survival prediction models in external validation settings.

    Science.gov (United States)

    Rahman, M Shafiqur; Ambler, Gareth; Choodari-Oskooei, Babak; Omar, Rumana Z

    2017-04-18

    When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell's concordance measure which tended to increase as censoring increased. We recommend that Uno's concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller's measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston's D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive accuracy curves. In addition, we recommend to investigate the characteristics

  5. Survival Advantage Associated with Metformin Usage in Hepatocellular Carcinoma Patients Receiving Radiotherapy: A Propensity Score Matching Analysis.

    Science.gov (United States)

    Jang, Won Il; Kim, Mi-Sook; Lim, Jung Sub; Yoo, Hyung Jun; Seo, Young Seok; Han, Chul Ju; Park, Su Cheol; Kay, Chul Seung; Kim, Myungsoo; Jang, Hong Seok; Lee, Dong Soo; Chang, Ah Ram; Park, Hae Jin

    2015-09-01

    The present study aimed to evaluate the effects of metformin on the clinical outcomes of patients receiving radiotherapy for inoperable hepatocellular carcinoma. The medical records of 217 patients treated with stereotactic body or hypofractionated radiotherapy for inoperable hepatocellular c