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Sample records for survival models adjusting

  1. 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.

  2. Covariate-adjusted measures of discrimination for survival data

    DEFF Research Database (Denmark)

    White, Ian R; Rapsomaniki, Eleni; Frikke-Schmidt, Ruth

    2015-01-01

    statistics in censored survival data. OBJECTIVE: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s). METHOD: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators......, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration. RESULTS: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were...

  3. 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...

  4. 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...

  5. 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.

  6. 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

  7. Convexity Adjustments for ATS Models

    DEFF Research Database (Denmark)

    Murgoci, Agatha; Gaspar, Raquel M.

    formulas. Concretely for LIBOR in arrears (LIA) contracts, we derive the system of Riccatti ODE-s one needs to compute to obtain the exact adjustment. Based upon the ideas of Schrager and Pelsser (2006) we are also able to derive general swap adjustments useful, in particular, when dealing with constant...

  8. Adjusting survival estimates for premature transmitter failure: A case study from the Sacramento-San Joaquin Delta

    Science.gov (United States)

    Holbrook, Christopher M.; Perry, Russell W.; Brandes, Patricia L.; Adams, Noah S.

    2013-01-01

    In telemetry studies, premature tag failure causes negative bias in fish survival estimates because tag failure is interpreted as fish mortality. We used mark-recapture modeling to adjust estimates of fish survival for a previous study where premature tag failure was documented. High rates of tag failure occurred during the Vernalis Adaptive Management Plan’s (VAMP) 2008 study to estimate survival of fall-run Chinook salmon (Oncorhynchus tshawytscha) during migration through the San Joaquin River and Sacramento-San Joaquin Delta, California. Due to a high rate of tag failure, the observed travel time distribution was likely negatively biased, resulting in an underestimate of tag survival probability in this study. Consequently, the bias-adjustment method resulted in only a small increase in estimated fish survival when the observed travel time distribution was used to estimate the probability of tag survival. Since the bias-adjustment failed to remove bias, we used historical travel time data and conducted a sensitivity analysis to examine how fish survival might have varied across a range of tag survival probabilities. Our analysis suggested that fish survival estimates were low (95% confidence bounds range from 0.052 to 0.227) over a wide range of plausible tag survival probabilities (0.48–1.00), and this finding is consistent with other studies in this system. When tags fail at a high rate, available methods to adjust for the bias may perform poorly. Our example highlights the importance of evaluating the tag life assumption during survival studies, and presents a simple framework for evaluating adjusted survival estimates when auxiliary travel time data are available.

  9. 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

  10. Kuk's Model Adjusted for Protection and Efficiency

    Science.gov (United States)

    Su, Shu-Ching; Sedory, Stephen A.; Singh, Sarjinder

    2015-01-01

    In this article, we adjust the Kuk randomized response model for collecting information on a sensitive characteristic for increased protection and efficiency by making use of forced "yes" and forced "no" responses. We first describe Kuk's model and then the proposed adjustment to Kuk's model. Next, by means of a simulation…

  11. 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

  12. 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

  13. Adjustment or updating of models

    Indian Academy of Sciences (India)

    Department of Mechanical Engineering, Imperial College of Science,. Technology and Medicine, London, UK email: d.ewins@ic.ac.uk ... and disadvantages of each, and the current state-of-the-art of this important application and part of optimum design technology. Keywords. Model updating; model correlation; analytical ...

  14. Children with cancer with different survival perspectives: defensiveness, control strategies, and psychological adjustment

    NARCIS (Netherlands)

    Grootenhuis, M. A.; Last, B. F.

    2001-01-01

    The main objective of the present study was to investigate whether children with cancer with different survival perspectives differ in their psychological adjustment, defensiveness and their use of cognitive control strategies. Furthermore, the study investigated which variables predict emotional

  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. Premium adjustment: actuarial analysis on epidemiological models ...

    African Journals Online (AJOL)

    In this paper, we analyse insurance premium adjustment in the context of an epidemiological model where the insurer's future financial liability is greater than the premium from patients. In this situation, it becomes extremely difficult for the insurer since a negative reserve would severely increase its risk of insolvency, ...

  17. 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...

  18. Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy.

    Science.gov (United States)

    Latimer, Nicholas R; Bell, Helen; Abrams, Keith R; Amonkar, Mayur M; Casey, Michelle

    2016-05-01

    Trametinib, a selective inhibitor of mitogen-activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression-free survival compared with chemotherapy in patients with BRAF V600E/K mutation-positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching-adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank-preserving structural failure time model, inverse probability of censoring weights, and a two-stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent-to-treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52-0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first-line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation-positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  19. Adjusting overall survival for treatment switches: commonly used methods and practical application.

    Science.gov (United States)

    Watkins, Claire; Huang, Xin; Latimer, Nicholas; Tang, Yiyun; Wright, Elaine J

    2013-01-01

    In parallel group trials, long-term efficacy endpoints may be affected if some patients switch or cross over to the alternative treatment arm prior to the event. In oncology trials, switch to the experimental treatment can occur in the control arm following disease progression and potentially impact overall survival. It may be a clinically relevant question to estimate the efficacy that would have been observed if no patients had switched, for example, to estimate 'real-life' clinical effectiveness for a health technology assessment. Several commonly used statistical methods are available that try to adjust time-to-event data to account for treatment switching, ranging from naive exclusion and censoring approaches to more complex inverse probability of censoring weighting and rank-preserving structural failure time models. These are described, along with their key assumptions, strengths, and limitations. Best practice guidance is provided for both trial design and analysis when switching is anticipated. Available statistical software is summarized, and examples are provided of the application of these methods in health technology assessments of oncology trials. Key considerations include having a clearly articulated rationale and research question and a well-designed trial with sufficient good quality data collection to enable robust statistical analysis. No analysis method is universally suitable in all situations, and each makes strong untestable assumptions. There is a need for further research into new or improved techniques. This information should aid statisticians and their colleagues to improve the design and analysis of clinical trials where treatment switch is anticipated. Copyright © 2013 John Wiley & Sons, Ltd.

  20. 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.

  1. Player Modeling for Intelligent Difficulty Adjustment

    Science.gov (United States)

    Missura, Olana; Gärtner, Thomas

    In this paper we aim at automatically adjusting the difficulty of computer games by clustering players into different types and supervised prediction of the type from short traces of gameplay. An important ingredient of video games is to challenge players by providing them with tasks of appropriate and increasing difficulty. How this difficulty should be chosen and increase over time strongly depends on the ability, experience, perception and learning curve of each individual player. It is a subjective parameter that is very difficult to set. Wrong choices can easily lead to players stopping to play the game as they get bored (if underburdened) or frustrated (if overburdened). An ideal game should be able to adjust its difficulty dynamically governed by the player’s performance. Modern video games utilise a game-testing process to investigate among other factors the perceived difficulty for a multitude of players. In this paper, we investigate how machine learning techniques can be used for automatic difficulty adjustment. Our experiments confirm the potential of machine learning in this application.

  2. 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.

  3. 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.

  4. 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.

  5. Methodological aspects of journaling a dynamic adjusting entry model

    Directory of Open Access Journals (Sweden)

    Vlasta Kašparovská

    2011-01-01

    Full Text Available This paper expands the discussion of the importance and function of adjusting entries for loan receivables. Discussion of the cyclical development of adjusting entries, their negative impact on the business cycle and potential solutions has intensified during the financial crisis. These discussions are still ongoing and continue to be relevant to members of the professional public, banking regulators and representatives of international accounting institutions. The objective of this paper is to evaluate a method of journaling dynamic adjusting entries under current accounting law. It also expresses the authors’ opinions on the potential for consistently implementing basic accounting principles in journaling adjusting entries for loan receivables under a dynamic model.

  6. 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.

  7. 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...

  8. Perfectionism, academic motivation, and psychological adjustment: an integrative model.

    Science.gov (United States)

    Miquelon, Paule; Vallerand, Robert J; Grouzet, Frédérick M E; Cardinal, Geneviève

    2005-07-01

    The purpose of this article was to propose and test an integrative model on the role of perfectionism, academic motivation, and psychological adjustment difficulties in undergraduate students. The model posits that self-oriented perfectionism facilitates self-determined academic motivation, whereas socially prescribed perfectionism enhances non-self-determined academic motivation. In turn, self-determined and non-self-determined academic motivations, respectively, lead to lower and higher levels of psychological adjustment difficulties. Results from two studies using structural equation modeling analyses provided support for the model.

  9. An approach to adjustment of relativistic mean field model parameters

    Science.gov (United States)

    Bayram, Tuncay; Akkoyun, Serkan

    2017-09-01

    The Relativistic Mean Field (RMF) model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN) method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs) of 58Ni and 208Pb have been found in agreement with the literature values.

  10. An approach to adjustment of relativistic mean field model parameters

    Directory of Open Access Journals (Sweden)

    Bayram Tuncay

    2017-01-01

    Full Text Available The Relativistic Mean Field (RMF model with a small number of adjusted parameters is powerful tool for correct predictions of various ground-state nuclear properties of nuclei. Its success for describing nuclear properties of nuclei is directly related with adjustment of its parameters by using experimental data. In the present study, the Artificial Neural Network (ANN method which mimics brain functionality has been employed for improvement of the RMF model parameters. In particular, the understanding capability of the ANN method for relations between the RMF model parameters and their predictions for binding energies (BEs of 58Ni and 208Pb have been found in agreement with the literature values.

  11. Extent of resection and survival in supratentorial infiltrative low-grade gliomas: analysis of and adjustment for treatment bias.

    Science.gov (United States)

    Gousias, Konstantinos; Schramm, Johannes; Simon, Matthias

    2014-02-01

    Any correlation between the extent of resection and the prognosis of patients with supratentorial infiltrative low-grade gliomas may well be related to biased treatment allocation. Patients with an intrinsically better prognosis may undergo more aggressive resections, and better survival may then be falsely attributed to the surgery rather than the biology of the disease. The present study investigates the potential impact of this type of treatment bias on survival in a series of patients with low-grade gliomas treated at the authors' institution. We conducted a retrospective study of 148 patients with low-grade gliomas undergoing primary treatment at our institution from 1996-2011. Potential prognostic factors were studied in order to identify treatment bias and to adjust survival analyses accordingly. Eloquence of tumor location proved the most powerful predictor of the extent of resection, i.e., the principal source of treatment bias. Univariate as well as multivariate Cox regression analyses identified the extent of resection and the presence of a preoperative neurodeficit as the most important predictors of overall survival, tumor recurrence and malignant progression. After stratification for eloquence of tumor location in order to correct for treatment bias, Kaplan-Meier estimates showed a consistent association between the degree of resection and improved survival. Treatment bias was not responsible for the correlation between extent of resection and survival observed in the present series. Our data seem to provide further support for a strategy of maximum safe resections for low-grade gliomas.

  12. Storm Water Management Model Climate Adjustment Tool (SWMM-CAT)

    Science.gov (United States)

    The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations. SWMM, first released in 1971, models hydrology and hydrauli...

  13. R.M. Solow Adjusted Model of Economic Growth

    Directory of Open Access Journals (Sweden)

    Ion Gh. Rosca

    2007-05-01

    Full Text Available Besides the models of M. Keynes, R.F. Harrod, E. Domar, D. Romer, Ramsey-Cass-Koopmans etc., the R.M. Solow model is part of the category which characterizes the economic growth. The paper proposes the study of the R.M. Solow adjusted model of economic growth, while the adjustment consisting in the model adaptation to the Romanian economic characteristics. The article is the first one from a three paper series dedicated to the macroeconomic modelling theme, using the R.M. Solow model, such as: “Measurement of the economic growth and extensions of the R.M. Solow adjusted model” and “Evolution scenarios at the Romanian economy level using the R.M. Solow adjusted model”. The analysis part of the model is based on the study of the equilibrium to the continuous case with some interpretations of the discreet one, by using the state diagram. The optimization problem at the economic level is also used; it is built up of a specified number of representative consumers and firms in order to reveal the interaction between these elements.

  14. Parenting Stress, Mental Health, Dyadic Adjustment: A Structural Equation Model

    Directory of Open Access Journals (Sweden)

    Luca Rollè

    2017-05-01

    Full Text Available Objective: In the 1st year of the post-partum period, parenting stress, mental health, and dyadic adjustment are important for the wellbeing of both parents and the child. However, there are few studies that analyze the relationship among these three dimensions. The aim of this study is to investigate the relationships between parenting stress, mental health (depressive and anxiety symptoms, and dyadic adjustment among first-time parents.Method: We studied 268 parents (134 couples of healthy babies. At 12 months post-partum, both parents filled out, in a counterbalanced order, the Parenting Stress Index-Short Form, the Edinburgh Post-natal Depression Scale, the State-Trait Anxiety Inventory, and the Dyadic Adjustment Scale. Structural equation modeling was used to analyze the potential mediating effects of mental health on the relationship between parenting stress and dyadic adjustment.Results: Results showed the full mediation effect of mental health between parenting stress and dyadic adjustment. A multi-group analysis further found that the paths did not differ across mothers and fathers.Discussion: The results suggest that mental health is an important dimension that mediates the relationship between parenting stress and dyadic adjustment in the transition to parenthood.

  15. Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

    Science.gov (United States)

    Boone, Spencer

    2017-01-01

    This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

  16. Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

    Directory of Open Access Journals (Sweden)

    Kimmel Marek

    2011-05-01

    Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

  17. 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....

  18. A simple approach to adjust tidal forcing in fjord models

    Science.gov (United States)

    Hjelmervik, Karina; Kristensen, Nils Melsom; Staalstrøm, André; Røed, Lars Petter

    2017-07-01

    To model currents in a fjord accurate tidal forcing is of extreme importance. Due to complex topography with narrow and shallow straits, the tides in the innermost parts of a fjord are both shifted in phase and altered in amplitude compared to the tides in the open water outside the fjord. Commonly, coastal tide information extracted from global or regional models is used on the boundary of the fjord model. Since tides vary over short distances in shallower waters close to the coast, the global and regional tidal forcings are usually too coarse to achieve sufficiently accurate tides in fjords. We present a straightforward method to remedy this problem by simply adjusting the tides to fit the observed tides at the entrance of the fjord. To evaluate the method, we present results from the Oslofjord, Norway. A model for the fjord is first run using raw tidal forcing on its open boundary. By comparing modelled and observed time series of water level at a tidal gauge station close to the open boundary of the model, a factor for the amplitude and a shift in phase are computed. The amplitude factor and the phase shift are then applied to produce adjusted tidal forcing at the open boundary. Next, we rerun the fjord model using the adjusted tidal forcing. The results from the two runs are then compared to independent observations inside the fjord in terms of amplitude and phases of the various tidal components, the total tidal water level, and the depth integrated tidal currents. The results show improvements in the modelled tides in both the outer, and more importantly, the inner parts of the fjord.

  19. Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

    Science.gov (United States)

    Patricia L. Andrews

    2012-01-01

    Rothermel's surface fire spread model was developed to use a value for the wind speed that affects surface fire, called midflame wind speed. Models have been developed to adjust 20-ft wind speed to midflame wind speed for sheltered and unsheltered surface fuel. In this report, Wind Adjustment Factor (WAF) model equations are given, and the BehavePlus fire modeling...

  20. Application and Performance Analysis of a New Bundle Adjustment Model

    Science.gov (United States)

    Sun, Y.; Liu, X.; Chen, R.; Wan, J.; Wang, Q.; Wang, H.; Li, Y.; Yan, L.

    2017-09-01

    As the basis for photogrammetry, Bundle Adjustment (BA) can restore the pose of cameras accurately, reconstruct the 3D models of environment, and serve as the criterion of digital production. For the classical nonlinear optimization of BA model based on the Euclidean coordinate, it suffers the problem of being seriously dependent on the initial values, making it unable to converge fast or converge to a global minimum. This paper first introduces a new BA model based on parallax angle feature parametrization, and then analyses the applications and performance of the model used in photogrammetry field. To discuss the impact and the performance of the model (especially in aerial photogrammetry), experiments using two aerial datasets under different initial values were conducted. The experiment results are better than some well-known software packages of BA, and the simulation results illustrate the stability of the new model than the normal BA under the Euclidean coordinate. In all, the new BA model shows promising applications in faster and more efficient aerial photogrammetry with good convergence and fast convergence speed.

  1. Constructing stochastic models from deterministic process equations by propensity adjustment.

    Science.gov (United States)

    Wu, Jialiang; Vidakovic, Brani; Voit, Eberhard O

    2011-11-08

    Gillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases. We propose a rather general strategy for converting a deterministic process model into a corresponding stochastic model and characterize the mathematical connections between the two. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations: where a direct conversion is valid; where internal noise affecting the system needs to be taken into account; and where the propensity function must be mathematically adjusted. The conversion from deterministic to stochastic models is illustrated with several representative examples, including reversible reactions with feedback controls, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. The construction of a stochastic model for a biochemical network requires the utilization

  2. Constructing stochastic models from deterministic process equations by propensity adjustment

    Directory of Open Access Journals (Sweden)

    Wu Jialiang

    2011-11-01

    Full Text Available Abstract Background Gillespie's stochastic simulation algorithm (SSA for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases. Results We propose a rather general strategy for converting a deterministic process model into a corresponding stochastic model and characterize the mathematical connections between the two. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations: where a direct conversion is valid; where internal noise affecting the system needs to be taken into account; and where the propensity function must be mathematically adjusted. The conversion from deterministic to stochastic models is illustrated with several representative examples, including reversible reactions with feedback controls, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. Conclusions The construction of a stochastic

  3. Constructing stochastic models from deterministic process equations by propensity adjustment

    Science.gov (United States)

    2011-01-01

    Background Gillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases. Results We propose a rather general strategy for converting a deterministic process model into a corresponding stochastic model and characterize the mathematical connections between the two. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations: where a direct conversion is valid; where internal noise affecting the system needs to be taken into account; and where the propensity function must be mathematically adjusted. The conversion from deterministic to stochastic models is illustrated with several representative examples, including reversible reactions with feedback controls, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. Conclusions The construction of a stochastic model for a biochemical

  4. Model for Adjustment of Aggregate Forecasts using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Taracena–Sanz L. F.

    2010-07-01

    Full Text Available This research suggests a contribution in the implementation of forecasting models. The proposed model is developed with the aim to fit the projection of demand to surroundings of firms, and this is based on three considerations that cause that in many cases the forecasts of the demand are different from reality, such as: 1 one of the problems most difficult to model in the forecasts is the uncertainty related to the information available; 2 the methods traditionally used by firms for the projection of demand mainly are based on past behavior of the market (historical demand; and 3 these methods do not consider in their analysis the factors that are influencing so that the observed behaviour occurs. Therefore, the proposed model is based on the implementation of Fuzzy Logic, integrating the main variables that affect the behavior of market demand, and which are not considered in the classical statistical methods. The model was applied to a bottling of carbonated beverages, and with the adjustment of the projection of demand a more reliable forecast was obtained.

  5. Risk-adjusted models for adverse obstetric outcomes and variation in risk-adjusted outcomes across hospitals.

    Science.gov (United States)

    Bailit, Jennifer L; Grobman, William A; Rice, Madeline Murguia; Spong, Catherine Y; Wapner, Ronald J; Varner, Michael W; Thorp, John M; Leveno, Kenneth J; Caritis, Steve N; Shubert, Phillip J; Tita, Alan T; Saade, George; Sorokin, Yoram; Rouse, Dwight J; Blackwell, Sean C; Tolosa, Jorge E; Van Dorsten, J Peter

    2013-11-01

    Regulatory bodies and insurers evaluate hospital quality using obstetrical outcomes, however meaningful comparisons should take preexisting patient characteristics into account. Furthermore, if risk-adjusted outcomes are consistent within a hospital, fewer measures and resources would be needed to assess obstetrical quality. Our objective was to establish risk-adjusted models for 5 obstetric outcomes and assess hospital performance across these outcomes. We studied a cohort of 115,502 women and their neonates born in 25 hospitals in the United States from March 2008 through February 2011. Hospitals were ranked according to their unadjusted and risk-adjusted frequency of venous thromboembolism, postpartum hemorrhage, peripartum infection, severe perineal laceration, and a composite neonatal adverse outcome. Correlations between hospital risk-adjusted outcome frequencies were assessed. Venous thromboembolism occurred too infrequently (0.03%; 95% confidence interval [CI], 0.02-0.04%) for meaningful assessment. Other outcomes occurred frequently enough for assessment (postpartum hemorrhage, 2.29%; 95% CI, 2.20-2.38, peripartum infection, 5.06%; 95% CI, 4.93-5.19, severe perineal laceration at spontaneous vaginal delivery, 2.16%; 95% CI, 2.06-2.27, neonatal composite, 2.73%; 95% CI, 2.63-2.84). Although there was high concordance between unadjusted and adjusted hospital rankings, several individual hospitals had an adjusted rank that was substantially different (as much as 12 rank tiers) than their unadjusted rank. None of the correlations between hospital-adjusted outcome frequencies was significant. For example, the hospital with the lowest adjusted frequency of peripartum infection had the highest adjusted frequency of severe perineal laceration. Evaluations based on a single risk-adjusted outcome cannot be generalized to overall hospital obstetric performance. Copyright © 2013 Mosby, Inc. All rights reserved.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. PERMINTAAN BERAS DI PROVINSI JAMBI (Penerapan Partial Adjustment Model

    Directory of Open Access Journals (Sweden)

    Wasi Riyanto

    2013-07-01

    Full Text Available The purpose of this study is to determine the effect of price of rice, flour prices, population, income of population and demand of rice for a year earlier on rice demand, demand rice elasticity and rice demand prediction in Jambi Province. This study uses secondary data, including time series data for 22 years from 1988 until 2009. The study used some variables, consist of rice demand (Qdt, the price of rice (Hb, the price of wheat flour (Hg, population (Jp, the income of the population (PDRB and demand for rice the previous year (Qdt-1. The make of this study are multiple regression and dynamic analysis a Partial Adjustment Model, where the demand for rice is the dependent variable and the price of rice, flour prices, population, income population and demand of rice last year was the independent variable. Partial Adjustment Model analysis results showed that the effect of changes in prices of rice and flour are not significant to changes in demand for rice. The population and demand of rice the previous year has positive and significant impact on demand for rice, while revenues have negative and significant population of rice demand. Variable price of rice, earning population and the price of flour is inelastic the demand of rice, because rice is not a normal good but as a necessity so that there is no substitution of goods (replacement of rice with other commodities in Jambi Province. Based on the analysis, it is recommended to the government to be able to control the rate of population increase given the variable number of people as one of the factors that affect demand for rice.It is expected that the government also began to socialize in a lifestyle of non-rice food consumption to control the increasing amount of demand for rice. Last suggestion, the government developed a diversification of staple foods other than rice.

  12. 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

  13. 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...

  14. Age-adjusted Charlson comorbidity index score as predictor of survival of patients with digestive system cancer who have undergone surgical resection.

    Science.gov (United States)

    Tian, Yaohua; Jian, Zhong; Xu, Beibei; Liu, Hui

    2017-10-03

    Comorbidities have considerable effects on survival outcomes. The primary objective of this retrospective study was to examine the association between age-adjusted Charlson comorbidity index (ACCI) score and postoperative in-hospital mortality in patients with digestive system cancer who have undergone surgical resection of their cancers. Using electronic hospitalization summary reports, we identified 315,464 patients who had undergone surgery for digestive system cancer in top-rank (Grade 3A) hospitals in China between 2013 and 2015. The Cox proportional hazard regression model was applied to evaluate the effect of ACCI score on postoperative mortality, with adjustments for sex, type of resection, anesthesia methods, and caseload of each healthcare institution. The postoperative in-hospital mortality rate in the study cohort was 1.2% (3,631/315,464). ACCI score had a positive graded association with the risk of postoperative in-hospital mortality for all cancer subtypes. The adjusted HRs for postoperative in-hospital mortality scores ≥ 6 for esophagus, stomach, colorectum, pancreas, and liver and gallbladder cancer were 2.05 (95% CI: 1.45-2.92), 2.00 (95% CI: 1.60-2.49), 2.54 (95% CI: 2.02-3.21), 2.58 (95% CI: 1.68-3.97), and 4.57 (95% CI: 3.37-6.20), respectively, compared to scores of 0-1. These findings suggested that a high ACCI score is an independent predictor of postoperative in-hospital mortality in Chinese patients with digestive system cancer who have undergone surgical resection.

  15. 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.

  16. Disaster Hits Home: A Model of Displaced Family Adjustment after Hurricane Katrina

    Science.gov (United States)

    Peek, Lori; Morrissey, Bridget; Marlatt, Holly

    2011-01-01

    The authors explored individual and family adjustment processes among parents (n = 30) and children (n = 55) who were displaced to Colorado after Hurricane Katrina. Drawing on in-depth interviews with 23 families, this article offers an inductive model of displaced family adjustment. Four stages of family adjustment are presented in the model: (a)…

  17. A Developmental Sequence Model to University Adjustment of International Undergraduate Students

    Science.gov (United States)

    Chavoshi, Saeid; Wintre, Maxine Gallander; Dentakos, Stella; Wright, Lorna

    2017-01-01

    The current study proposes a Developmental Sequence Model to University Adjustment and uses a multifaceted measure, including academic, social and psychological adjustment, to examine factors predictive of undergraduate international student adjustment. A hierarchic regression model is carried out on the Student Adaptation to College Questionnaire…

  18. 40-year trends in an index of survival for all cancers combined and survival adjusted for age and sex for each cancer in England and Wales, 1971-2011: a population-based study.

    Science.gov (United States)

    Quaresma, Manuela; Coleman, Michel P; Rachet, Bernard

    2015-03-28

    Assessment of progress in cancer control at the population level is increasingly important. Population-based survival trends provide a key insight into the overall effectiveness of the health system, alongside trends in incidence and mortality. For this purpose, we aimed to provide a unique measure of cancer survival. In this observational study, we analysed trends in survival with population-based data for 7·2 million adults diagnosed with a first, primary, invasive malignancy in England and Wales during 1971-2011 and followed up to the end of 2012. We constructed a survival index for all cancers combined using data from the National Cancer Registry and the Welsh Cancer Intelligence and Surveillance Unit. The index is designed to be independent of changes in the age distribution of patients with cancer and of changes in the proportion of lethal cancers in each sex. We analysed trends in the cancer survival index at 1, 5, and 10 years after diagnosis for the selected periods 1971-72, 1980-81, 1990-91, 2000-01, 2005-06, and 2010-11. We also estimated trends in age-sex-adjusted survival for each cancer. We define the difference in net survival between the oldest (75-99 years) and youngest (15-44 years) patients as the age gap in survival. We evaluated the absolute change (%) in the age gap since 1971. The overall index of net survival increased substantially during the 40-year period 1971-2011, both in England and in Wales. For patients diagnosed in 1971-72, the index of net survival was 50% at 1 year after diagnosis. 40 years later, the same value of 50% was predicted at 10 years after diagnosis. The average 10% survival advantage for women persisted throughout this period. Predicted 10-year net survival adjusted for age and sex for patients diagnosed between 2010 and 2011 ranged from 1·1% for pancreatic cancer to 98·2% for testicular cancer. Net survival for the oldest patients (75-99 years) was persistently lower than for the youngest (15-44 years), even after

  19. 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

  20. 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

  1. A Developmental Sequence Model to University Adjustment of International Undergraduate Students

    National Research Council Canada - National Science Library

    Saeid Chavoshi; Maxine Gallander Wintre; Stella Dentakos; Lorna Wright

    2017-01-01

    Keywords: adjustment to university, developmental sequence model, emerging adulthood, undergraduate international students The presence of international students in our institutions provides a host...

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  6. 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...

  7. 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.

  8. 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.

  9. R.M. Solow Adjusted Model of Economic Growth

    Directory of Open Access Journals (Sweden)

    Ion Gh. Rosca

    2007-05-01

    The analysis part of the model is based on the study of the equilibrium to the continuous case with some interpretations of the discreet one, by using the state diagram. The optimization problem at the economic level is also used; it is built up of a specified number of representative consumers and firms in order to reveal the interaction between these elements.

  10. Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry.

    Science.gov (United States)

    Meyer, Andrew J; Patten, Carolynn; Fregly, Benjamin J

    2017-01-01

    Neuromusculoskeletal disorders affecting walking ability are often difficult to manage, in part due to limited understanding of how a patient's lower extremity muscle excitations contribute to the patient's lower extremity joint moments. To assist in the study of these disorders, researchers have developed electromyography (EMG) driven neuromusculoskeletal models utilizing scaled generic musculoskeletal geometry. While these models can predict individual muscle contributions to lower extremity joint moments during walking, the accuracy of the predictions can be hindered by errors in the scaled geometry. This study presents a novel EMG-driven modeling method that automatically adjusts surrogate representations of the patient's musculoskeletal geometry to improve prediction of lower extremity joint moments during walking. In addition to commonly adjusted neuromusculoskeletal model parameters, the proposed method adjusts model parameters defining muscle-tendon lengths, velocities, and moment arms. We evaluated our EMG-driven modeling method using data collected from a high-functioning hemiparetic subject walking on an instrumented treadmill at speeds ranging from 0.4 to 0.8 m/s. EMG-driven model parameter values were calibrated to match inverse dynamic moments for five degrees of freedom in each leg while keeping musculoskeletal geometry close to that of an initial scaled musculoskeletal model. We found that our EMG-driven modeling method incorporating automated adjustment of musculoskeletal geometry predicted net joint moments during walking more accurately than did the same method without geometric adjustments. Geometric adjustments improved moment prediction errors by 25% on average and up to 52%, with the largest improvements occurring at the hip. Predicted adjustments to musculoskeletal geometry were comparable to errors reported in the literature between scaled generic geometric models and measurements made from imaging data. Our results demonstrate that with

  11. 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

  12. 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.

  13. Examining Competing Models of the Associations among Peer Victimization, Adjustment Problems, and School Connectedness

    Science.gov (United States)

    Loukas, Alexandra; Ripperger-Suhler, Ken G.; Herrera, Denise E.

    2012-01-01

    The present study tested two competing models to assess whether psychosocial adjustment problems mediate the associations between peer victimization and school connectedness one year later, or if peer victimization mediates the associations between psychosocial adjustment problems and school connectedness. Participants were 500 10- to 14-year-old…

  14. ASPECTS OF DESIGN PROCESS AND CAD MODELLING OF AN ADJUSTABLE CENTRIFUGAL COUPLING

    Directory of Open Access Journals (Sweden)

    Adrian BUDALĂ

    2015-05-01

    Full Text Available The paper deals with constructive and functional elements of an adjustable coupling with friction shoes and adjustable driving. Also, the paper shows few stages of the design process, some advantages of the using CAD software and some comparative results prototype vs. CAD model.

  15. Droop Control with an Adjustable Complex Virtual Impedance Loop based on Cloud Model Theory

    DEFF Research Database (Denmark)

    Li, Yan; Shuai, Zhikang; Xu, Qinming

    2016-01-01

    Droop control framework with an adjustable virtual impedance loop is proposed in this paper, which is based on the cloud model theory. The proposed virtual impedance loop includes two terms: a negative virtual resistor and an adjustable virtual inductance. The negative virtual resistor term...

  16. 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.

  17. 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.

  18. Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling.

    Science.gov (United States)

    Payne, Elizabeth H; Hardin, James W; Egede, Leonard E; Ramakrishnan, Viswanathan; Selassie, Anbesaw; Gebregziabher, Mulugeta

    2017-08-01

    Overdispersion is a common problem in count data. It can occur due to extra population-heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can lead to invalid inference. Our goal is to assess the differential performance of methods for dealing with overdispersion from several sources. We considered six different approaches: unadjusted Poisson regression (Poisson), deviance-scale-adjusted Poisson regression (DS-Poisson), Pearson-scale-adjusted Poisson regression (PS-Poisson), negative-binomial regression (NB), and two generalized linear mixed models (GLMM) with random intercept, log-link and Poisson (Poisson-GLMM) and negative-binomial (NB-GLMM) distributions. To rank order the preference of the models, we used Akaike's information criteria/Bayesian information criteria values, standard error, and 95% confidence-interval coverage of the parameter values. To compare these methods, we used simulated count data with overdispersion of different magnitude from three different sources. Mean of the count response was associated with three predictors. Data from two real-case studies are also analyzed. The simulation results showed that NB and NB-GLMM were preferred for dealing with overdispersion resulting from any of the sources we considered. Poisson and DS-Poisson often produced smaller standard-error estimates than expected, while PS-Poisson conversely produced larger standard-error estimates. Thus, it is good practice to compare several model options to determine the best method of modeling count data.

  19. A New Climate Adjustment Tool: An update to EPA’s Storm Water Management Model

    Science.gov (United States)

    The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations.

  20. 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.

  1. 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...

  2. How patients experience progressive loss of visual function: a model of adjustment using qualitative methods.

    Science.gov (United States)

    Hayeems, R Z; Geller, G; Finkelstein, D; Faden, R R

    2005-05-01

    People with retinitis pigmentosa (RP) experience functional and psychological challenges as they adjust to progressive loss of visual function. The authors aimed to understand better the process of adjusting to RP in light of the emotional suffering associated with this process. Adults with RP were recruited from the Foundation Fighting Blindness and the Wilmer Eye Institute in Baltimore. Focus groups and semistructured interviews addressed the process of adjusting to RP and were audiotaped and transcribed. The transcripts were analysed qualitatively in order to generate a model of adjustment. A total of 43 individuals participated. It was found that, on diagnosis, people with RP seek to understand its meaning in their lives. Mastering the progressive functional implications associated with RP is contingent upon shifting personal identity from a sighted to a visually impaired person. In this sample, six participants self identified as sighted, 10 self identified as in transition, and 27 self identified as visually impaired. This adjustment process can be understood in terms of a five stage model of behaviour change. The proposed model presents one way to understand the process of adjusting to RP and could assist ophthalmologists in meeting their moral obligation to lessen patients' suffering, which arises in the course of their adjustment to progressive loss of visual function.

  3. Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

    Science.gov (United States)

    W. Brad Smith

    1983-01-01

    A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

  4. Modeling and Control of the Redundant Parallel Adjustment Mechanism on a Deployable Antenna Panel

    Directory of Open Access Journals (Sweden)

    Lili Tian

    2016-10-01

    Full Text Available With the aim of developing multiple input and multiple output (MIMO coupling systems with a redundant parallel adjustment mechanism on the deployable antenna panel, a structural control integrated design methodology is proposed in this paper. Firstly, the modal information from the finite element model of the structure of the antenna panel is extracted, and then the mathematical model is established with the Hamilton principle; Secondly, the discrete Linear Quadratic Regulator (LQR controller is added to the model in order to control the actuators and adjust the shape of the panel. Finally, the engineering practicality of the modeling and control method based on finite element analysis simulation is verified.

  5. 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.

  6. 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

  7. A structural equation modeling approach to the study of stress and psychological adjustment in emerging adults.

    Science.gov (United States)

    Asberg, Kia K; Bowers, Clint; Renk, Kimberly; McKinney, Cliff

    2008-12-01

    Today's society puts constant demands on the time and resources of all individuals, with the resulting stress promoting a decline in psychological adjustment. Emerging adults are not exempt from this experience, with an alarming number reporting excessive levels of stress and stress-related problems. As a result, the present study addresses the need for a comprehensive model of emerging adult adjustment in the context of stress and coping variables and highlights the importance of accounting for differences between males and females in research concerning stress, social support, coping, and adjustment. Participants for this study are 239 college students (122 males and 117 females), the majority of whom are Caucasian. Results of structural equation modeling suggest that stress, social support, coping, and adjustment show unique patterns of relationships for males versus females. For both males and females, stress and social support show similar relationships to adjustment. In contrast, social support is related only to coping behaviors in females. Finally, social support appears to be a more important variable for female adjustment, whereas other coping behaviors appear to be more pertinent to male adjustment. Limitations and suggestions for future research will be discussed.

  8. On the hydrologic adjustment of climate-model projections: The potential pitfall of potential evapotranspiration

    Science.gov (United States)

    Milly, P.C.D.; Dunne, K.A.

    2011-01-01

    Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.

  9. School Adjustment in the Early Grades: Toward an Integrated Model of Neighborhood, Parental, and Child Processes

    Science.gov (United States)

    Nettles, Saundra Murray; Caughy, Margaret O'Brien; O'Campo, Patricia J.

    2008-01-01

    Examining recent research on neighborhood influences on child development, this review focuses on social influences on school adjustment in the early elementary years. A model to guide community research and intervention is presented. The components of the model of integrated processes are neighborhoods and their effects on academic outcomes and…

  10. Modeling Quality-Adjusted Life Expectancy Loss Resulting from Tobacco Use in the United States

    Science.gov (United States)

    Kaplan, Robert M.; Anderson, John P.; Kaplan, Cameron M.

    2007-01-01

    Purpose: To describe the development of a model for estimating the effects of tobacco use upon Quality Adjusted Life Years (QALYs) and to estimate the impact of tobacco use on health outcomes for the United States (US) population using the model. Method: We obtained estimates of tobacco consumption from 6 years of the National Health Interview…

  11. Evaluation of the Stress Adjustment and Adaptation Model among Families Reporting Economic Pressure

    Science.gov (United States)

    Vandsburger, Etty; Biggerstaff, Marilyn A.

    2004-01-01

    This research evaluates the Stress Adjustment and Adaptation Model (double ABCX model) examining the effects resiliency resources on family functioning when families experience economic pressure. Families (N = 128) with incomes at or below the poverty line from a rural area of a southern state completed measures of perceived economic pressure,…

  12. Emotional closeness to parents and grandparents: A moderated mediation model predicting adolescent adjustment.

    Science.gov (United States)

    Attar-Schwartz, Shalhevet

    2015-09-01

    Warm and emotionally close relationships with parents and grandparents have been found in previous studies to be linked with better adolescent adjustment. The present study, informed by Family Systems Theory and Intergenerational Solidarity Theory, uses a moderated mediation model analyzing the contribution of the dynamics of these intergenerational relationships to adolescent adjustment. Specifically, it examines the mediating role of emotional closeness to the closest grandparent in the relationship between emotional closeness to a parent (the offspring of the closest grandparent) and adolescent adjustment difficulties. The model also examines the moderating role of emotional closeness to parents in the relationship between emotional closeness to grandparents and adjustment difficulties. The study was based on a sample of 1,405 Jewish Israeli secondary school students (ages 12-18) who completed a structured questionnaire. It was found that emotional closeness to the closest grandparent was more strongly associated with reduced adjustment difficulties among adolescents with higher levels of emotional closeness to their parents. In addition, adolescent adjustment and emotional closeness to parents was partially mediated by emotional closeness to grandparents. Examining the family conditions under which adolescents' relationships with grandparents is stronger and more beneficial for them can help elucidate variations in grandparent-grandchild ties and expand our understanding of the mechanisms that shape child outcomes. (c) 2015 APA, all rights reserved).

  13. Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards models.

    Science.gov (United States)

    Richardson, David B; Laurier, Dominique; Schubauer-Berigan, Mary K; Tchetgen Tchetgen, Eric; Cole, Stephen R

    2014-11-01

    Workers' smoking histories are not measured in many occupational cohort studies. Here we discuss the use of negative control outcomes to detect and adjust for confounding in analyses that lack information on smoking. We clarify the assumptions necessary to detect confounding by smoking and the additional assumptions necessary to indirectly adjust for such bias. We illustrate these methods using data from 2 studies of radiation and lung cancer: the Colorado Plateau cohort study (1950-2005) of underground uranium miners (in which smoking was measured) and a French cohort study (1950-2004) of nuclear industry workers (in which smoking was unmeasured). A cause-specific relative hazards model is proposed for estimation of indirectly adjusted associations. Among the miners, the proposed method suggests no confounding by smoking of the association between radon and lung cancer--a conclusion supported by adjustment for measured smoking. Among the nuclear workers, the proposed method suggests substantial confounding by smoking of the association between radiation and lung cancer. Indirect adjustment for confounding by smoking resulted in an 18% decrease in the adjusted estimated hazard ratio, yet this cannot be verified because smoking was unmeasured. Assumptions underlying this method are described, and a cause-specific proportional hazards model that allows easy implementation using standard software is presented. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. Suggestion of a Numerical Model for the Blood Glucose Adjustment with Ingesting a Food

    Science.gov (United States)

    Yamamoto, Naokatsu; Takai, Hiroshi

    In this study, we present a numerical model of the time dependence of blood glucose value after ingesting a meal. Two numerical models are proposed in this paper to explain a digestion mechanism and an adjustment mechanism of blood glucose in the body, respectively. It is considered that models are exhibited by using simple equations with a transfer function and a block diagram. Additionally, the time dependence of blood glucose was measured, when subjects ingested a sucrose or a starch. As a result, it is clear that the calculated result of models using a computer can be fitted very well to the measured result of the time dependence of blood glucose. Therefore, it is considered that the digestion model and the adjustment model are useful models in order to estimate a blood glucose value after ingesting meals.

  15. 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.

  16. Conference Innovations in Derivatives Market : Fixed Income Modeling, Valuation Adjustments, Risk Management, and Regulation

    CERN Document Server

    Grbac, Zorana; Scherer, Matthias; Zagst, Rudi

    2016-01-01

    This book presents 20 peer-reviewed chapters on current aspects of derivatives markets and derivative pricing. The contributions, written by leading researchers in the field as well as experienced authors from the financial industry, present the state of the art in: • Modeling counterparty credit risk: credit valuation adjustment, debit valuation adjustment, funding valuation adjustment, and wrong way risk. • Pricing and hedging in fixed-income markets and multi-curve interest-rate modeling. • Recent developments concerning contingent convertible bonds, the measuring of basis spreads, and the modeling of implied correlations. The recent financial crisis has cast tremendous doubts on the classical view on derivative pricing. Now, counterparty credit risk and liquidity issues are integral aspects of a prudent valuation procedure and the reference interest rates are represented by a multitude of curves according to their different periods and maturities. A panel discussion included in the book (featuring D...

  17. 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

  18. Rational Multi-curve Models with Counterparty-risk Valuation Adjustments

    DEFF Research Database (Denmark)

    Crépey, Stéphane; Macrina, Andrea; Nguyen, Tuyet Mai

    2016-01-01

    We develop a multi-curve term structure set-up in which the modelling ingredients are expressed by rational functionals of Markov processes. We calibrate to London Interbank Offer Rate swaptions data and show that a rational two-factor log-normal multi-curve model is sufficient to match market da...... with regulatory obligations. In order to compute counterparty-risk valuation adjustments, such as credit valuation adjustment, we show how default intensity processes with rational form can be derived. We flesh out our study by applying the results to a basis swap contract....

  19. Understanding property market dynamics: insights from modelling the supply-side adjustment mechanism

    OpenAIRE

    Nanda Nanthakumaran; Craig Watkins; Allison Orr

    2000-01-01

    The volatility of commercial property markets in the United Kingdomhas stimulated the development of explanatory models of 'price' determination. These models have tended to focus on the demand-side as the driver of change. A corollary of this is that, despite the fact that construction lags are known to exacerbate cyclical fluctuations, the supply-side adjustment mechanism has been subject to relatively little research effort. In this paper the authors develop a new model of commercial prope...

  20. Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

    Science.gov (United States)

    Haem, Elham; Harling, Kajsa; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Karlsson, Mats O

    2017-02-01

    One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

  1. A reassessment of the PRIMO recommendations for adjustments to mid-latitude ionospheric models

    Science.gov (United States)

    David, M.; Sojka, J. J.; Schunk, R. W.

    2012-12-01

    In the late 1990s, in response to the realization that ionospheric physical models tended to underestimate the dayside peak F-region electron density (NmF2) by about a factor of 2, a group of modelers convened to find out why. The project was dubbed PRIMO, standing for Problems Relating to Ionospheric Models and Observations. Five ionospheric models were employed in the original study, including the Utah State University Time Dependent Ionospheric Model (TDIM), which is the focus of the present study. No physics-based explanation was put forward for the models' shortcomings, but there was a recommendation that three adjustments be made within the models: 1) The inclusion of a Burnside factor of 1.7 for the diffusion coefficients; 2) that the branching ratio of O+ be changed from 0.38 to 0.25; and 3) that the dayside ion production rates be scaled upward to account for ionization by secondary photons. The PRIMO recommendations were dutifully included in our TDIM model at Utah State University, though as time went on, and particularly while modeling the ionosphere during the International Polar Year (2007), it became clear that the PRIMO adjustments sometimes caused the model to produce excessively high dayside electron densities. As the original PRIMO study [Anderson et al, 1998] was based upon model/observation comparison over a very limited set of observations from just one station (Millstone Hill, Massachusetts), we have expanded the range of the study, taking advantage of resources that were not available 12 years ago, most notably the NGDC SPIDR Internet data base, and faster computers for running large numbers of simulations with the TDIM model. We look at ionosonde measurements of the peak dayside electron densities at mid-latitudes around the world, across the full range of seasons and solar cycles, as well as levels of geomagnetic activity, in order to determine at which times the PRIMO adjustments should be included in the model, and when it is best not to

  2. 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.

  3. 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

  4. Designing a model to improve first year student adjustment to university

    Directory of Open Access Journals (Sweden)

    Nasrin Nikfal Azar

    2014-05-01

    Full Text Available The increase in the number of universities for the last decade in Iran increases the need for higher education institutions to manage their enrollment, more effectively. The purpose of this study is to design a model to improve the first year university student adjustment by examining the effects of academic self-efficacy, academic motivation, satisfaction, high school GPA and demographic variables on student’s adjustment to university. The study selects a sample of 357 students out of 4585 bachelor first year student who were enrolled in different programs. Three questionnaires were used for collection of data for this study, namely academic self-efficacy, academic motivation and student satisfaction with university. Structural equation modeling was employed using AMOS version7.16 to test the adequacy of the hypothesized model. Inclusion of additional relationship in the initial model improved the goodness indices considerably. The results suggest that academic self-efficacy were related positively to adjustment, both directly (B=0.35 and indirectly through student satisfaction (B=0.14 and academic motivation (B=0.9. The results indicate a need to develop programs that effectively promote the self-efficacy of first year student of student to increase college adjustment and consequently retention rate.

  5. Work Adjustment, Disability, and the Three R's of Vocational Rehabilitation: A Conceptual Model.

    Science.gov (United States)

    Hershenson, David B.

    1981-01-01

    Presents a model of work adjustment consisting of work personality, work competencies, and work goals. Appropriate interventions include restoration of work-related skills, remotivation to address the impact of disability on work personality, and restructuring of work goals as necessitated by the handicap. (Author/JAC)

  6. Glacial isostatic adjustment model with composite 3-D Earth rheology for Fennoscandia

    NARCIS (Netherlands)

    Wal, W. van der; Barnhoorn, A.; Stocchi, P.; Gradmann, S.; Wu, P.; Drury, M.R.; Vermeersen, B.

    2013-01-01

    Models for glacial isostatic adjustment (GIA) can provide constraints on rheology of the mantle if past ice thickness variations are assumed to be known. The Pleistocene ice loading histories that are used to obtain such constraints are based on an a priori 1-D mantle viscosity profile that assumes

  7. 10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables.

    Science.gov (United States)

    Abad, Cesar C C; Barros, Ronaldo V; Bertuzzi, Romulo; Gagliardi, João F L; Lima-Silva, Adriano E; Lambert, Mike I; Pires, Flavio O

    2016-06-01

    The aim of this study was to verify the power of VO 2max , peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO 2max and PTV; 2) a constant submaximal run at 12 km·h -1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO 2max , PTV and RE) and adjusted variables (VO 2max 0.72 , PTV 0.72 and RE 0.60 ) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO 2max . Significant correlations (p 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV 0.72 and RE 0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.

  8. A Threshold Model of Social Support, Adjustment, and Distress after Breast Cancer Treatment

    Science.gov (United States)

    Mallinckrodt, Brent; Armer, Jane M.; Heppner, P. Paul

    2012-01-01

    This study examined a threshold model that proposes that social support exhibits a curvilinear association with adjustment and distress, such that support in excess of a critical threshold level has decreasing incremental benefits. Women diagnosed with a first occurrence of breast cancer (N = 154) completed survey measures of perceived support…

  9. Testing an Attachment Model of Latina/o College Students' Psychological Adjustment

    Science.gov (United States)

    Garriott, Patton O.; Love, Keisha M.; Tyler, Kenneth M.; Thomas, Deneia M.; Roan-Belle, Clarissa R.; Brown, Carrie L.

    2010-01-01

    The present study examined the influence of attachment relationships on the psychological adjustment of Latina/o university students (N = 80) attending predominantly White institutions of higher education. A path analysis conducted to test a hypothesized model of parent and peer attachment, self-esteem, and psychological distress indicated that…

  10. 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.

  11. Higher convection volume exchange with online hemodiafiltration is associated with survival advantage for dialysis patients: the effect of adjustment for body size.

    Science.gov (United States)

    Davenport, Andrew; Peters, Sanne A E; Bots, Michiel L; Canaud, Bernard; Grooteman, Muriel P C; Asci, Gulay; Locatelli, Francesco; Maduell, Francisco; Morena, Marion; Nubé, Menso J; Ok, Ercan; Torres, Ferran; Woodward, Mark; Blankestijn, Peter J

    2016-01-01

    Mortality remains high for hemodialysis patients. Online hemodiafiltration (OL-HDF) removes more middle-sized uremic toxins but outcomes of individual trials comparing OL-HDF with hemodialysis have been discrepant. Secondary analyses reported higher convective volumes, easier to achieve in larger patients, and improved survival. Here we tested different methods to standardize OL-HDF convection volume on all-cause and cardiovascular mortality compared with hemodialysis. Pooled individual patient analysis of four prospective trials compared thirds of delivered convection volume with hemodialysis. Convection volumes were either not standardized or standardized to weight, body mass index, body surface area, and total body water. Data were analyzed by multivariable Cox proportional hazards modeling from 2793 patients. All-cause mortality was reduced when the convective dose was unstandardized or standardized to body surface area and total body water; hazard ratio (95% confidence intervals) of 0.65 (0.51-0.82), 0.74 (0.58-0.93), and 0.71 (0.56-0.93) for those receiving higher convective doses. Standardization by body weight or body mass index gave no significant survival advantage. Higher convection volumes were generally associated with greater survival benefit with OL-HDF, but results varied across different ways of standardization for body size. Thus, further studies should take body size into account when evaluating the impact of delivered convection volume on mortality end points. Copyright © 2015 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

  12. 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

  13. GENERAL MATHEMATICAL MODEL OF LEAST SQUARES 3D SURFACE MATCHING AND ITS APPLICATION OF STRIP ADJUSTMENT

    Directory of Open Access Journals (Sweden)

    Z. Q. Zuo

    2012-07-01

    Full Text Available Systematic errors in point clouds acquired by airborne laser scanners, photogrammetric methods or other 3D measurement techniques need to be estimated and removed by adjustment procedures. The proposed method estimates the transformation parameters between reference surface and registration surface using a mathematical adjustment model. 3D surface matching is an extension of 2D least squares image matching. The estimation model is a typical Gauss-Markoff model and the goal is minimizing the sum of squares of the Euclidean distances between the contiguous surfaces. Besides the generic mathematical model, we also propose a concept of conjugate points rules which are suitable for special registering applications, and compare it to three typical conjugate points rules. Finally, we explain how this method can be used for the co-registration of real 3D point sets and show co-registration results based on airborne laser scanner data. Concluding results of our experiment suggest that the proposed method has a good performance of 3D surface matching, and the least normal distance rule returns the best result for the strip adjustment of airborne laser altimetry data.

  14. 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...

  15. Modelling goal adjustment in social relationships: Two experimental studies with children and adults.

    Science.gov (United States)

    Thomsen, Tamara; Kappes, Cathleen; Schwerdt, Laura; Sander, Johanna; Poller, Charlotte

    2017-06-01

    In two experiments, we investigated observational learning in social relationships as one possible pathway to the development of goal adjustment processes. In the first experiment, 56 children (M = 9.29 years) observed their parent as a model; in the second, 50 adults (M = 32.27 years) observed their romantic partner. Subjects were randomly assigned to three groups: goal engagement (GE), goal disengagement (GD), or control group (CO) and were asked to solve (unsolvable) puzzles. Before trying to solve the puzzles by themselves, subjects observed the instructed model, who was told to continue with the same puzzle (GE) or to switch to the next puzzle (GD). Results show that children in the GE group switched significantly less than in the GD or CO group. There was no difference between the GD group and CO group. Adults in the GE group switched significantly less than in the GD or CO group, whereas subjects in the GD group switched significantly more often than the CO group. Statement of contribution What is already known on this subject? Previous research focused mainly on the functions of goal adjustment processes. It rarely considered processes and conditions that contribute to the development of goal engagement and goal disengagement. There are only two cross-sectional studies that directly investigate this topic. Previous research that claims observational learning as a pathway of learning emotion regulation or adjustment processes has (only) relied on correlational methods and, thus, do not allow any causal interpretations. Previous research, albeit claiming a life span focus, mostly investigated goal adjustment processes in one specific age group (mainly adults). There is no study that investigates the same processes in different age groups. What does this study add? In our two studies, we focus on the conditions of goal adjustment processes and sought to demonstrate one potential pathway of learning or changing the application of goal adjustment

  16. [Applying temporally-adjusted land use regression models to estimate ambient air pollution exposure during pregnancy].

    Science.gov (United States)

    Zhang, Y J; Xue, F X; Bai, Z P

    2017-03-06

    The impact of maternal air pollution exposure on offspring health has received much attention. Precise and feasible exposure estimation is particularly important for clarifying exposure-response relationships and reducing heterogeneity among studies. Temporally-adjusted land use regression (LUR) models are exposure assessment methods developed in recent years that have the advantage of having high spatial-temporal resolution. Studies on the health effects of outdoor air pollution exposure during pregnancy have been increasingly carried out using this model. In China, research applying LUR models was done mostly at the model construction stage, and findings from related epidemiological studies were rarely reported. In this paper, the sources of heterogeneity and research progress of meta-analysis research on the associations between air pollution and adverse pregnancy outcomes were analyzed. The methods of the characteristics of temporally-adjusted LUR models were introduced. The current epidemiological studies on adverse pregnancy outcomes that applied this model were systematically summarized. Recommendations for the development and application of LUR models in China are presented. This will encourage the implementation of more valid exposure predictions during pregnancy in large-scale epidemiological studies on the health effects of air pollution in China.

  17. The Pennsylvania Trauma Outcomes Study Risk-Adjusted Mortality Model: Results of a Statewide Benchmarking Program.

    Science.gov (United States)

    Wiebe, Douglas J; Holena, Daniel N; Delgado, M Kit; McWilliams, Nathan; Altenburg, Juliet; Carr, Brendan G

    2017-05-01

    Trauma centers need objective feedback on performance to inform quality improvement efforts. The Trauma Quality Improvement Program recently published recommended methodology for case mix adjustment and benchmarking performance. We tested the feasibility of applying this methodology to develop risk-adjusted mortality models for a statewide trauma system. We performed a retrospective cohort study of patients ≥16 years old at Pennsylvania trauma centers from 2011 to 2013 (n = 100,278). Our main outcome measure was observed-to-expected mortality ratios (overall and within blunt, penetrating, multisystem, isolated head, and geriatric subgroups). Patient demographic variables, physiology, mechanism of injury, transfer status, injury severity, and pre-existing conditions were included as predictor variables. The statistical model had excellent discrimination (area under the curve = 0.94). Funnel plots of observed-to-expected identified five centers with lower than expected mortality and two centers with higher than expected mortality. No centers were outliers for management of penetrating trauma, but five centers had lower and three had higher than expected mortality for blunt trauma. It is feasible to use Trauma Quality Improvement Program methodology to develop risk-adjusted models for statewide trauma systems. Even with smaller numbers of trauma centers that are available in national datasets, it is possible to identify high and low outliers in performance.

  18. Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.

    Science.gov (United States)

    Qiao, Hong; Xi, Xuanyang; Li, Yinlin; Wu, Wei; Li, Fengfu

    2015-11-01

    Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational

  19. 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 ...

  20. 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 ...

  1. Analyses adjusting for selective crossover show improved overall survival with adjuvant letrozole compared with tamoxifen in the BIG 1-98 study.

    Science.gov (United States)

    Colleoni, Marco; Giobbie-Hurder, Anita; Regan, Meredith M; Thürlimann, Beat; Mouridsen, Henning; Mauriac, Louis; Forbes, John F; Paridaens, Robert; Láng, István; Smith, Ian; Chirgwin, Jacquie; Pienkowski, Tadeusz; Wardley, Andrew; Price, Karen N; Gelber, Richard D; Coates, Alan S; Goldhirsch, Aron

    2011-03-20

    Among postmenopausal women with endocrine-responsive breast cancer, the aromatase inhibitor letrozole, when compared with tamoxifen, has been shown to significantly improve disease-free survival (DFS) and time to distant recurrence (TDR). We investigated whether letrozole monotherapy prolonged overall survival (OS) compared with tamoxifen monotherapy. Of 8,010 postmenopausal women with hormone receptor-positive, early breast cancer enrolled on the Breast International Group (BIG) 1-98 study, 4,922 were randomly assigned to 5 years of continuous adjuvant therapy with either letrozole or tamoxifen. Of 2,459 patients enrolled in the tamoxifen treatment arm, 619 (25.2%) selectively crossed over to either adjuvant or extended letrozole after initial trial results were presented in January 2005. To gain better estimates of relative treatment effects in the presence of selective crossover, we used inverse probability of censoring weighted (IPCW) modeling. Weighted Cox models, by using IPCW, estimated a statistically significant, 18% reduction in the hazard of an OS event with letrozole treatment (hazard ratio [HR], 0.82; 95% CI, 0.70 to 0.95). Estimates of 5-year OS on the basis of IPCW were 91.8% and 90.4% for letrozole and tamoxifen, respectively. The HRs of DFS and TDR events by using IPCW modeling were 0.83 (95% CI, 0.74 to 0.94) and 0.80 (95% CI, 0.67 to 0.94), respectively (P < .05 for DFS, OS, and TDR). Median follow-up was 74 months. Adjuvant treatment with letrozole, compared with tamoxifen, significantly reduces the risk of death, the risk of recurrent disease, and the risk of recurrence at distant sites in postmenopausal women with hormone receptor-positive breast cancer.

  2. Interfacial free energy adjustable phase field crystal model for homogeneous nucleation.

    Science.gov (United States)

    Guo, Can; Wang, Jincheng; Wang, Zhijun; Li, Junjie; Guo, Yaolin; Huang, Yunhao

    2016-05-18

    To describe the homogeneous nucleation process, an interfacial free energy adjustable phase-field crystal model (IPFC) was proposed by reconstructing the energy functional of the original phase field crystal (PFC) methodology. Compared with the original PFC model, the additional interface term in the IPFC model effectively can adjust the magnitude of the interfacial free energy, but does not affect the equilibrium phase diagram and the interfacial energy anisotropy. The IPFC model overcame the limitation that the interfacial free energy of the original PFC model is much less than the theoretical results. Using the IPFC model, we investigated some basic issues in homogeneous nucleation. From the viewpoint of simulation, we proceeded with an in situ observation of the process of cluster fluctuation and obtained quite similar snapshots to colloidal crystallization experiments. We also counted the size distribution of crystal-like clusters and the nucleation rate. Our simulations show that the size distribution is independent of the evolution time, and the nucleation rate remains constant after a period of relaxation, which are consistent with experimental observations. The linear relation between logarithmic nucleation rate and reciprocal driving force also conforms to the steady state nucleation theory.

  3. Multiple arterial grafting confers survival advantage compared to percutaneous intervention with drug-eluting stents in multivessel coronary artery disease: A propensity score adjusted analysis.

    Science.gov (United States)

    Raja, Shahzad G; Benedetto, Umberto; Ilsley, Charles D; Amrani, Mohamed

    2015-01-01

    The best revascularisation strategy for multivessel coronary artery disease (MVD) is still controversial. Percutaneous coronary intervention (PCI) utilising drug eluting stents (DES) has emerged as an acceptable alternative to conventional coronary artery bypass grafting (CABG) in the last decade. However, multiple arterial grafting (MAG) is superior revascularisation strategy compared with conventional CABG utilising single internal mammary artery and currently there is a paucity of comparison of DES and MAG. We aimed to investigate whether MAG offers advantage over DES-PCI in MVD. A total of 6126 patients with MVD (≥ 2 vessel) underwent CABG (n = 4652) or PCI (n = 1474) at a single institution. MAG was performed in 1372 CABG cases and DES were implanted in 1222 PCI cases. Propensity score adjusted analysis was performed to investigate the potential survival advantage of MAG over PCI. Mean follow-up was 4.9 years. Risk for late death was comparable after DES-PCI and conventional CABG (HR 1.11; 95%CI 0.9 to 1.33; P = 0.25). However, DES-PCI was associated with an increased risk for late death compared to MAG (HR 1.53; 95%CI 1.08 to 2.91; P = 0.02). DES-PCI was also associated with a 3.51 fold increased risk for repeat revascularisation over MAG (95%CI 2.60 to 4.75; P compared to DES-PCI. When feasible, MAG should be strongly recommended in patients with MVD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. Rational Multi-Curve Models with Counterparty-Risk Valuation Adjustments

    DEFF Research Database (Denmark)

    Crepey, Stephane; Macrina, Andrea; Nguyen, Tuyet Mai

    2016-01-01

    We develop a multi-curve term structure setup in which the modelling ingredients are expressed by rational functionals of Markov processes. We calibrate to LIBOR swaptions data and show that a rational two-factor lognormal multi-curve model is sufficient to match market data with accuracy. We elu...... obligations. In order to compute counterparty-risk valuation adjustments, such as CVA, we show how positive default intensity processes with rational form can be derived. We flesh out our study by applying the results to a basis swap contract....

  5. 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.

  6. Adjustment to severe disability: constructing and examining a cognitive and occupational performance model.

    Science.gov (United States)

    Schreuer, Naomi; Rimmerman, Arie; Sachs, Dalia

    2006-09-01

    Ninety adults with severe physical disabilities were tested with respect to their adjustment to severe disabilities in their adapted computerized work environment 1 year following occupational therapy consultation. The research goal was to examine a model that incorporates variables from the cognitive coping model (self-esteem, appraisal and social support) and variables from the occupational performance model (engagement in activities, involvement in work/study, time of typing performance and environmental adaptations). Findings showed goodness of fit between the observed and the proposed research models, although few changes in positions and relations were found. Self-esteem and time of performance were found to be core variables connecting cognitive and functional variables. Age and activities of daily living were the only background variables that contributed to the model. Research and rehabilitation clinical implications are discussed.

  7. 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.

  8. Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

    Science.gov (United States)

    Siman-Tov, Ayelet; Kaniel, Shlomo

    2011-01-01

    The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

  9. “A model of mother-child Adjustment in Arab Muslim Immigrants to the US”

    Science.gov (United States)

    Hough, Edythe s; Templin, Thomas N; Kulwicki, Anahid; Ramaswamy, Vidya; Katz, Anne

    2009-01-01

    We examined the mother-child adjustment and child behavior problems in Arab Muslim immigrant families residing in the U.S.A. The sample of 635 mother-child dyads was comprised of mothers who emigrated from 1989 or later and had at least one early adolescent child between the ages of 11 to 15 years old who was also willing to participate. Arabic speaking research assistants collected the data from the mothers and children using established measures of maternal and child stressors, coping, and social support; maternal distress; parent-child relationship; and child behavior problems. A structural equation model (SEM) was specified a priori with 17 predicted pathways. With a few exceptions, the final SEM model was highly consistent with the proposed model and had a good fit to the data. The model accounted for 67% of the variance in child behavior problems. Child stressors, mother-child relationship, and maternal stressors were the causal variables that contributed the most to child behavior problems. The model also accounted for 27% of the variance in mother-child relationship. Child active coping, child gender, mother’s education, and maternal distress were all predictive of the mother-child relationship. Mother-child relationship also mediated the effects of maternal distress and child active coping on child behavior problems. These findings indicate that immigrant mothers contribute greatly to adolescent adjustment, both as a source of risk and protection. These findings also suggest that intervening with immigrant mothers to reduce their stress and strengthening the parent-child relationship are two important areas for promoting adolescent adjustment. PMID:19758737

  10. A model of mother-child adjustment in Arab Muslim immigrants to the US.

    Science.gov (United States)

    Aroian, Karen; Hough, Edythe S; Templin, Thomas N; Kulwicki, Anahid; Ramaswamy, Vidya; Katz, Anne

    2009-11-01

    We examined the mother-child adjustment and child behavior problems in Arab Muslim immigrant families residing in the U.S.A. The sample of 635 mother-child dyads was comprised of mothers who emigrated from 1989 or later and had at least one early adolescent child between the ages of 11 and 15 years old who was also willing to participate. Arabic speaking research assistants collected the data from the mothers and children using established measures of maternal and child stressors, coping, and social support; maternal distress; parent-child relationship; and child behavior problems. A structural equation model (SEM) was specified a priori with 17 predicted pathways. With a few exceptions, the final SEM model was highly consistent with the proposed model and had a good fit to the data. The model accounted for 67% of the variance in child behavior problems. Child stressors, mother-child relationship, and maternal stressors were the causal variables that contributed the most to child behavior problems. The model also accounted for 27% of the variance in mother-child relationship. Child active coping, child gender, mother's education, and maternal distress were all predictive of the mother-child relationship. Mother-child relationship also mediated the effects of maternal distress and child active coping on child behavior problems. These findings indicate that immigrant mothers contribute greatly to adolescent adjustment, both as a source of risk and protection. These findings also suggest that intervening with immigrant mothers to reduce their stress and strengthening the parent-child relationship are two important areas for promoting adolescent adjustment.

  11. Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction

    Science.gov (United States)

    Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.

    2015-12-01

    Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.

  12. 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.

  13. Impact of Abuse on Adjustment and Chronic Pain Disability: A Structural Equation Model.

    Science.gov (United States)

    Mehta, Swati; Rice, Danielle; Chan, Alan; Shapiro, Allan P; Sequeira, Keith; Teasell, Robert W

    2017-08-01

    Sexual abuse, state and trait psychosocial factors, pain intensity, and pain-related disability have been shown to be correlated among individuals with chronic pain. However, the interacting relationships among these factors are poorly understood. The current study aims to test model which examines the effect of abuse, state and trait psychosocial factors, and pain intensity on pain-related disability among individuals with chronic pain. In total, 229 participants diagnosed with chronic pain were recruited from a specialist chronic pain hospital in London, Ontario. Participants completed self-report measures related to sexual abuse history, pain intensity, personality (anxiety sensitivity, experiential avoidance, perfectionism), and adjustment (depression, anxiety, disability, maladaptive worrying). A path analysis was used to test the relationship among these variables. The model provided a close fit to the data (χ21=17.02; P=0.71; root-mean-square error of approximation=0.00; normal fit index=0.97; comparative fit index=1.0). The model demonstrates the direct and indirect effects of childhood sexual abuse on state and trait psychosocial factors among individuals with chronic pain. Pain anxiety, maladaptive worrying, and pain intensity were the main determinants of pain-related disability. The current model has important implications in understanding the interplay of factors involved in adjustment of individuals with chronic pain. Sexual abuse did not have a direct effect on pain-related disability. However, indirect effects through other psychosocial factors were demonstrated.

  14. 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

  15. 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.

  16. A molecular collision operator of adjustable direction for the discrete velocity direction model

    Science.gov (United States)

    Zhang, Zhenyu; Peng, Cheng; Xu, Jianzhong

    2017-10-01

    The discrete velocity direction model is an approximate method to the Boltzmann equation. A developed molecular collision operator for the model is presented in this paper. Under the new operator, the discrete directions of molecules are adjustable, namely, both the number and the angles of discrete directions can be changed as needed in the discrete velocity direction model. At the same time, the governing equations will keep unchanged when the number of discrete directions changes. In fact, with the continuous molecular speed, the discrete velocity direction model has been able to employ any discrete velocities in numerical calculations. The discrete velocity direction model under the new collision operator was applied into some benchmark flows in micro scales in this paper, and the influence of the number of discrete velocities on the computational accuracy was analyzed. The numerical results show that the accuracy of the discrete velocity direction model can be improved significantly by employing more discrete directions, especially for the gas flows at large Knudsen number. With appropriate discrete velocities, this model has been able to give accurate numerical results in all flow regimes. In addition, it is proved that the discrete velocity direction model under the new collision operator satisfies a global H theorem unconditionally, which means that the new operator further improves the intrinsic stability of the discrete velocity direction model.

  17. Multiplicative random regression model for heterogeneous variance adjustment in genetic evaluation for milk yield in Simmental.

    Science.gov (United States)

    Lidauer, M H; Emmerling, R; Mäntysaari, E A

    2008-06-01

    A multiplicative random regression (M-RRM) test-day (TD) model was used to analyse daily milk yields from all available parities of German and Austrian Simmental dairy cattle. The method to account for heterogeneous variance (HV) was based on the multiplicative mixed model approach of Meuwissen. The variance model for the heterogeneity parameters included a fixed region x year x month x parity effect and a random herd x test-month effect with a within-herd first-order autocorrelation between test-months. Acceleration of variance model solutions after each multiplicative model cycle enabled fast convergence of adjustment factors and reduced total computing time significantly. Maximum Likelihood estimation of within-strata residual variances was enhanced by inclusion of approximated information on loss in degrees of freedom due to estimation of location parameters. This improved heterogeneity estimates for very small herds. The multiplicative model was compared with a model that assumed homogeneous variance. Re-estimated genetic variances, based on Mendelian sampling deviations, were homogeneous for the M-RRM TD model but heterogeneous for the homogeneous random regression TD model. Accounting for HV had large effect on cow ranking but moderate effect on bull ranking.

  18. Test of an adjustable pitch model propeller at four blade settings

    Science.gov (United States)

    Lesley, E P

    1930-01-01

    This note describes tests of an adjustable blade metal model propeller, both in a free wind stream and in combination with a model fuselage, at four settings of the blades. The model propeller is designed for a uniform nominal pitch/diameter ratio of .7 and the blade settings used correspond to nominal pitch/diameter ratios of .5, .7, .9, and 1.1 at the .6 radius. The tests show that propellers of this type may be considerably changed in setting from the designed pitch angles and yet give excellent performance. The efficiency realized and power absorbed when blades are set at other than the designed angle, are little different than would be obtained from a propeller with uniform pitch equal to the mean pitch of the propeller under test.

  19. Empiric model for mean generation time adjustment factor for classic point kinetics equations

    Energy Technology Data Exchange (ETDEWEB)

    Goes, David A.B.V. de; Martinez, Aquilino S.; Goncalves, Alessandro da C., E-mail: david.goes@poli.ufrj.br, E-mail: aquilino@lmp.ufrj.br, E-mail: alessandro@con.ufrj.br [Coordenacao de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Departamento de Engenharia Nuclear

    2017-11-01

    Point reactor kinetics equations are the easiest way to observe the neutron production time behavior in a nuclear reactor. These equations are derived from the neutron transport equation using an approximation called Fick's law leading to a set of first order differential equations. The main objective of this study is to review classic point kinetics equation in order to approximate its results to the case when it is considered the time variation of the neutron currents. The computational modeling used for the calculations is based on the finite difference method. The results obtained with this model are compared with the reference model and then it is determined an empirical adjustment factor that modifies the point reactor kinetics equation to the real scenario. (author)

  20. Latent class modelling of the association between socioeconomic background and breast cancer survival status at 5 years incorporating stage of disease.

    Science.gov (United States)

    Downing, Amy; Harrison, Wendy J; West, Robert M; Forman, David; Gilthorpe, Mark S

    2010-09-01

    Stage of disease and socioeconomic background (SEB) are often used to 'explain' differences in breast cancer outcomes. There are challenges for all types of analysis (eg, survival analysis, logistic regression), including missing data, measurement error and the 'reversal paradox'. This study investigates the association between SEB and survival status within 5 years of breast cancer diagnosis using (1) logistic regression with and without adjustment for stage and (2) logistic latent class analysis (LCA) excluding stage as a covariate but with and without stage as a latent class predictor. Women diagnosed with invasive breast cancer between 1998 and 2000 in one UK region were identified (n=11 781). Multilevel logistic regression was performed using standard regression and LCA. Models included SEB (2001 Townsend Index), age and stage ('missing' stage (8.0%) modelled as a separate category). The association of SEB with stage was also assessed. Using standard regression, there was a substantial association between SEB and death within 5 years, with and without adjustment for stage. Using LCA, patients were assigned to a large good prognosis group and a small poor prognosis group. The association between SEB and survival was substantive in both classes for the model without stage, but only in the larger class for the model with stage. Increasing deprivation was associated with more advanced stage at diagnosis. LCA categorises patients into prognostic groups according to patient and tumour characteristics, providing an alternative strategy to the usual statistical adjustment for stage.

  1. Uncertainties in Tidally Adjusted Estimates of Sea Level Rise Flooding (Bathtub Model for the Greater London

    Directory of Open Access Journals (Sweden)

    Ali P. Yunus

    2016-04-01

    Full Text Available Sea-level rise (SLR from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC fifth assessment report (AR5 and UK climatic projections 2009 (UKCP09 using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs.

  2. 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.

  3. A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data

    DEFF Research Database (Denmark)

    He, Peng; Eriksson, Frank; Scheike, Thomas H.

    2016-01-01

    function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate-adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate-adjusted weight...

  4. A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment).

    Science.gov (United States)

    Passamonti, Francesco; Cervantes, Francisco; Vannucchi, Alessandro Maria; Morra, Enrica; Rumi, Elisa; Pereira, Arturo; Guglielmelli, Paola; Pungolino, Ester; Caramella, Marianna; Maffioli, Margherita; Pascutto, Cristiana; Lazzarino, Mario; Cazzola, Mario; Tefferi, Ayalew

    2010-03-04

    Age older than 65 years, hemoglobin level lower than 100 g/L (10 g/dL), white blood cell count greater than 25 x 10(9)/L, peripheral blood blasts 1% or higher, and constitutional symptoms have been shown to predict poor survival in primary myelofibrosis (PMF) at diagnosis. To investigate whether the acquisition of these factors during follow-up predicts survival, we studied 525 PMF patients regularly followed. All 5 variables had a significant impact on survival when analyzed as time-dependent covariates in a multivariate Cox proportional hazard model and were included in 2 separate models, 1 for all patients (Dynamic International Prognostic Scoring System [DIPSS]) and 1 for patients younger than 65 years (age-adjusted DIPSS). Risk factors were assigned score values based on hazard ratios (HRs). Risk categories were low, intermediate-1, intermediate-2, and high in both models. Survival was estimated by the HR. When shifting to the next risk category, the HR was 4.13 for low risk, 4.61 for intermediate-1, and 2.54 for intermediate-2 according to DIPSS; 3.97 for low risk, 2.84 for intermediate-1, and 1.81 for intermediate-2 according to the age-adjusted DIPSS. The novelty of these models is the prognostic assessment of patients with PMF anytime during their clinical course, which may be useful for treatment decision-making.

  5. 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.

  6. 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...

  7. 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.

  8. Rejection, Feeling Bad, and Being Hurt: Using Multilevel Modeling to Clarify the Link between Peer Group Aggression and Adjustment

    Science.gov (United States)

    Rulison, Kelly L.; Gest, Scott D.; Loken, Eric; Welsh, Janet A.

    2010-01-01

    The association between affiliating with aggressive peers and behavioral, social and psychological adjustment was examined. Students initially in 3rd, 4th, and 5th grade (N = 427) were followed biannually through 7th grade. Students’ peer-nominated groups were identified. Multilevel modeling was used to examine the independent contributions of adolescents’ typical peer context (between-person effect) and changes in peer context (within-person effects) to adolescents’ adjustment. Typically affiliating with aggressive groups and affiliating with more aggressive groups than usual predicted higher aggression for all youth. Typically affiliating with aggressive groups predicted negative adjustment (lower social preference and self-worth, higher victimization) for girls but neutral or positive adjustment for boys. Although typical peer context was consistently associated with adjustment, changes in peer context predicted small changes in adjustment for several outcomes. Results underscored the need to adopt a more differentiated picture of adolescents’ dynamic peer context and its association with normative development. PMID:20832107

  9. 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.

  10. 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.

  11. 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

  12. The Trauma Outcome Process Assessment Model: A Structural Equation Model Examination of Adjustment

    Science.gov (United States)

    Borja, Susan E.; Callahan, Jennifer L.

    2009-01-01

    This investigation sought to operationalize a comprehensive theoretical model, the Trauma Outcome Process Assessment, and test it empirically with structural equation modeling. The Trauma Outcome Process Assessment reflects a robust body of research and incorporates known ecological factors (e.g., family dynamics, social support) to explain…

  13. Measurement of the Economic Growth and Add-on of the R.M. Solow Adjusted Model

    Directory of Open Access Journals (Sweden)

    Ion Gh. Rosca

    2007-08-01

    Full Text Available Besides the models of M. Keynes, R.F. Harrod, E. Domar, D. Romer, Ramsey-Cass-Koopmans model etc., the R.M. Solow model is part of the category which characterizes the economic growth.The paper aim is the economic growth measurement and add-on of the R.M. Solow adjusted model.

  14. 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.

  15. 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

  16. A Comparative Study of CAPM and Seven Factors Risk Adjusted Return Model

    Directory of Open Access Journals (Sweden)

    Madiha Riaz Bhatti

    2014-12-01

    Full Text Available This study is a comparison and contrast of the predictive powers of two asset pricing models: CAPM and seven factor risk-return adjusted model, to explain the cross section of stock rate of returns in the financial sector listed at Karachi Stock Exchange (KSE. To test the models daily returns from January 2013 to February 2014 have been taken and the excess returns of portfolios are regressed on explanatory variables. The results of the tested models indicate that the models are valid and applicable in the financial market of Pakistan during the period under study, as the intercepts are not significantly different from zero. It is consequently established from the findings that all the explanatory variables explain the stock returns in the financial sector of KSE. In addition, the results of this study show that addition of more explanatory variables to the single factor CAPM results in reasonably high values of R2. These results provide substantial support to fund managers, investors and financial analysts in making investment decisions.

  17. 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...

  18. Analyses adjusting for selective crossover show improved overall survival with adjuvant letrozole compared with tamoxifen in the BIG 1-98 study

    DEFF Research Database (Denmark)

    Colleoni, Marco; Giobbie-Hurder, Anita; Regan, Meredith M

    2011-01-01

    Among postmenopausal women with endocrine-responsive breast cancer, the aromatase inhibitor letrozole, when compared with tamoxifen, has been shown to significantly improve disease-free survival (DFS) and time to distant recurrence (TDR). We investigated whether letrozole monotherapy prolonged...

  19. 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.

  20. 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.

  1. Adjustment of regional climate model output for modeling the climatic mass balance of all glaciers on Svalbard.

    Science.gov (United States)

    Möller, Marco; Obleitner, Friedrich; Reijmer, Carleen H; Pohjola, Veijo A; Głowacki, Piotr; Kohler, Jack

    2016-05-27

    Large-scale modeling of glacier mass balance relies often on the output from regional climate models (RCMs). However, the limited accuracy and spatial resolution of RCM output pose limitations on mass balance simulations at subregional or local scales. Moreover, RCM output is still rarely available over larger regions or for longer time periods. This study evaluates the extent to which it is possible to derive reliable region-wide glacier mass balance estimates, using coarse resolution (10 km) RCM output for model forcing. Our data cover the entire Svalbard archipelago over one decade. To calculate mass balance, we use an index-based model. Model parameters are not calibrated, but the RCM air temperature and precipitation fields are adjusted using in situ mass balance measurements as reference. We compare two different calibration methods: root mean square error minimization and regression optimization. The obtained air temperature shifts (+1.43°C versus +2.22°C) and precipitation scaling factors (1.23 versus 1.86) differ considerably between the two methods, which we attribute to inhomogeneities in the spatiotemporal distribution of the reference data. Our modeling suggests a mean annual climatic mass balance of -0.05 ± 0.40 m w.e. a-1 for Svalbard over 2000-2011 and a mean equilibrium line altitude of 452 ± 200 m  above sea level. We find that the limited spatial resolution of the RCM forcing with respect to real surface topography and the usage of spatially homogeneous RCM output adjustments and mass balance model parameters are responsible for much of the modeling uncertainty. Sensitivity of the results to model parameter uncertainty is comparably small and of minor importance.

  2. ELECTRICAL CONDUCTIVITY OF SOYBEAN SEED CULTIVARS AND ADJUSTED MODELS OF LEAKAGE CURVES ALONG THE TIME

    Directory of Open Access Journals (Sweden)

    ADRIANA RITA SALINAS

    2010-01-01

    Full Text Available The objective of this work was to study the behavior of ten soybean [Glycine max (L. Merr.] cultivars using the electrical conductivity (EC test by the comparison of curves of the accumulative electrolyte leakage along the time and to establish the statistical model that allow the best adjust of the curves. Ten soybean cultivars were used and they were mechanically harvested in 2004 in the EEA Oliveros, Santa Fe, Argentina. Measurements of EC were made for 100 individual seeds of each cultivar during 20 hours of immersion at intervals of 1 hour using an equipment that permit an individual seed analysis (Seed Automatic Analyzer SAD 9000S. There were proposed two statistical models to study the EC along the time of the 10 cultivars studied using SAS Statistics Program, to select the model that better allow us to understand the EC behavior along the time. Model 1 allowed to make comparisons of EC along the time between cultivars and to study the influence of the production environment on the physiological quality of soybean seeds. The time to reach the stabilization of the EC must not be lower than 19 hours for the different cultivars.

  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. 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

  5. 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.

  6. The use of satellites in gravity field determination and model adjustment

    Science.gov (United States)

    Visser, Petrus Nicolaas Anna Maria

    1992-06-01

    Methods to improve gravity field models of the Earth with available data from satellite observations are proposed and discussed. In principle, all types of satellite observations mentioned give information of the satellite orbit perturbations and in conjunction the Earth's gravity field, because the satellite orbits are affected most by the Earth's gravity field. Therefore, two subjects are addressed: representation forms of the gravity field of the Earth and the theory of satellite orbit perturbations. An analytical orbit perturbation theory is presented and shown to be sufficiently accurate for describing satellite orbit perturbations if certain conditions are fulfilled. Gravity field adjustment experiments using the analytical orbit perturbation theory are discussed using real satellite observations. These observations consisted of Seasat laser range measurements and crossover differences, and of Geosat altimeter measurements and crossover differences. A look into the future, particularly relating to the ARISTOTELES (Applications and Research Involving Space Techniques for the Observation of the Earth's field from Low Earth Orbit Spacecraft) mission, is given.

  7. 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

  8. 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.

  9. 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.

  10. Location memory for dots in polygons versus cities in regions: evaluating the category adjustment model.

    Science.gov (United States)

    Friedman, Alinda; Montello, Daniel R; Burte, Heather

    2012-09-01

    We conducted 3 experiments to examine the category adjustment model (Huttenlocher, Hedges, & Duncan, 1991) in circumstances in which the category boundaries were irregular schematized polygons made from outlines of maps. For the first time, accuracy was tested when only perceptual and/or existing long-term memory information about identical locations was cued. Participants from Alberta, Canada and California received 1 of 3 conditions: dots-only, in which a dot appeared within the polygon, and after a 4-s dynamic mask the empty polygon appeared and the participant indicated where the dot had been; dots-and-names, in which participants were told that the first polygon represented Alberta/California and that each dot was in the correct location for the city whose name appeared outside the polygon; and names-only, in which there was no first polygon, and participants clicked on the city locations from extant memory alone. Location recall in the dots-only and dots-and-names conditions did not differ from each other and had small but significant directional errors that pointed away from the centroids of the polygons. In contrast, the names-only condition had large and significant directional errors that pointed toward the centroids. Experiments 2 and 3 eliminated the distribution of stimuli and overall screen position as causal factors. The data suggest that in the "classic" category adjustment paradigm, it is difficult to determine a priori when Bayesian cue combination is applicable, making Bayesian analysis less useful as a theoretical approach to location estimation. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  11. 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....

  12. Positive Adjustment Among American Repatriated Prisoners of the Vietnam War: Modeling the Long-Term Effects of Captivity.

    Science.gov (United States)

    King, Daniel W; King, Lynda A; Park, Crystal L; Lee, Lewina O; Kaiser, Anica Pless; Spiro, Avron; Moore, Jeffrey L; Kaloupek, Danny G; Keane, Terence M

    2015-11-01

    A longitudinal lifespan model of factors contributing to later-life positive adjustment was tested on 567 American repatriated prisoners from the Vietnam War. This model encompassed demographics at time of capture and attributes assessed after return to the U.S. (reports of torture and mental distress) and approximately 3 decades later (later-life stressors, perceived social support, positive appraisal of military experiences, and positive adjustment). Age and education at time of capture and physical torture were associated with repatriation mental distress, which directly predicted poorer adjustment 30 years later. Physical torture also had a salutary effect, enhancing later-life positive appraisals of military experiences. Later-life events were directly and indirectly (through concerns about retirement) associated with positive adjustment. Results suggest that the personal resources of older age and more education and early-life adverse experiences can have cascading effects over the lifespan to impact well-being in both positive and negative ways.

  13. 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.

  14. Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

    Directory of Open Access Journals (Sweden)

    Greenwood Celia MT

    2005-05-01

    Full Text Available Abstract Background With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups. Results We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously. Conclusion Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

  15. Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment

    Science.gov (United States)

    Tahmasebi, Pejman; Sahimi, Muhammad

    2016-03-01

    In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.

  16. 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.

  17. Impact of two adjustable-autonomy models on the scalability of single-human/multiple-robot teams for exploration missions.

    Science.gov (United States)

    Valero-Gomez, Alberto; de la Puente, Paloma; Hernando, Miguel

    2011-12-01

    The aim of this study was to evaluate two models for adjusting autonomy in mobile robots to find out the best way for the operator to interact with the system with as many robots as possible. The first model is the most used in mobile robots; the second proposes a flexible autonomy management. There are different ways of adjusting the autonomy level in man-machine systems: adjustable autonomy, in which the operator has the initiative over the autonomy level; adaptive autonomy, in which the autonomy level is adjusted depending on the task and context; and mixed initiatives. One of the drawbacks of using adjustable autonomy is that it is claimed not to be flexible enough, resulting in a high operator workload. We propose and evaluate a flexible adjustable autonomy model for robot-team supervision. Two experiments were designed to test the scalability and performance of the man-machine system with two alternative configurations for the autonomy management. The independent variable is the number of robots, and the measured variable is the man-machine system performance. The experiments are between subjects. We have used ANOVA and Bonferroni post hoc analysis for analyzing the results. On the basis of these analyses,we conclude that a flexible adjustable autonomy model results in better performance than the classic, rigid one, in which the operator directly chooses the autonomy level. Flexible autonomy adjustment permits one operator to control a team of robots with better results in terms of performance and robot use, as he or she can directly act at the error level, leaving the responsibility of readjusting and resuming the task to the system and hence reducing the operator's workload. The results can be applied to exploration robotics, mainly, in which one operator controls a team of robots. In general, these principles can be extended to other single-man/multiple-machine systems.

  18. 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.

  19. 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

  20. Relationships among self-esteem, job adjustment and service attitude amongst male nurses: a structural equation model.

    Science.gov (United States)

    Lou, Jiunn-Horng; Li, Ren-Hau; Yu, Hsing-Yi; Chen, Sheng-Hwang

    2011-03-01

    The purpose of this study was to test a relationship model of self-esteem and job adjustment affecting the service attitude of male nurses in Taiwan. Service attitude of nurses may affect their intention to leave and the quality of health care for their patients. Self-esteem is an important predictor of service attitude. Very few researchers have assessed job adjustment and service attitude in male nurses. Reducing job stress and burnout would help to increase job satisfaction. A cross-sectional research design was used in this study. In 2009, 338 male nurses were invited to participate in this study. Finally, 284 participants completed the questionnaire, and the response rate was 84.0%. The results revealed that the postulated model fits the data from this study well. Self-esteem did not significantly correlate to service attitude. Job adjustment was a significantly influencing factor on service attitude. We conclude that job adjustment was indeed important in explaining the service attitude of male nurses. Nurse managers must help male nurses adjust their job as soon as possible to improve their service attitude. In clinical practice and management, our findings provide concrete directions for nursing management and professionals in helping male nurses adjust to their job to improve male nurses' service attitude. More clinical situation must be provided and practiced before male nursing students graduate from school. We suggest future study is needed to generalise this model to different populations. © 2010 Blackwell Publishing Ltd.

  1. How patients experience progressive loss of visual function: a model of adjustment using qualitative methods

    OpenAIRE

    Hayeems, R Z; Geller, G.; Finkelstein, D; Faden, R. R.

    2005-01-01

    Background: People with retinitis pigmentosa (RP) experience functional and psychological challenges as they adjust to progressive loss of visual function. The authors aimed to understand better the process of adjusting to RP in light of the emotional suffering associated with this process.

  2. Crustal motion measurements from the POLENET Antarctic Network: comparisons with glacial isostatic adjustment models

    Science.gov (United States)

    Wilson, T. J.; Konfal, S. A.; Bevis, M. G.; Spada, G.; Melini, D.; Barletta, V. R.; Kendrick, E. C.; Saddler, D.; Smalley, R., Jr.; Dalziel, I. W. D.; Willis, M. J.

    2016-12-01

    Crustal motions measured by GPS provide a unique proxy record of ice mass change, due to the elastic and viscoelastic response of the earth to removal of ice loads. The ANET/POLENET array of bedrock GPS sites spans much of the Antarctic interior, encompassing regions where glacial isostatic adjustment (GIA) models predict large crustal displacements due to LGM ice loss and including coastal West Antarctica where major modern ice mass loss is documented. To isolate the long-term GIA component of measured crustal motions, we computed and removed elastic displacements due to recent ice mass change. We used the annually resolved ice mass balance data from Martín-Español et al. (2016) derived from a statistical inversion of satellite altimetry, gravimetry, and elastic-corrected GPS data for the period 2003-2013. The Regional Elastic Rebound Calculator (REAR) [Melini et al., 2015] was used to compute elastic vertical and horizontal surface displacements. Uplift due to elastic rebound is substantial in West Antarctica, very minimal in East Antarctica, and variable across the Weddell Embayment. The ANET GPS-derived crustal motion patterns ascribed to non-elastic GIA are spatially complex and differ significantly in magnitude from model predictions. We present a systematic comparison of measured and predicted velocities within different sectors of Antarctica, in order to examine spatial patterns relative to modern ice mass changes, ice history model uncertainties, and lateral variations in earth properties. In the Weddell Embayment region most vertical velocities are lower than uplift predicted by GIA models. Several sites in the southernmost Transantarctic Mountains and the Whitmore Mountains, where small ice mass increase occurs, have vertical uplift significantly exceeding GIA model predictions. There is an intriguing spatial correlation of these fast-moving sites with a low-velocity anomaly in the upper mantle documented by analysis of teleseismic Rayleigh waves by

  3. 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

  4. Assessing climate change effects on long-term forest development: adjusting growth, phenology, and seed production in a gap model

    NARCIS (Netherlands)

    Meer, van der P.J.; Jorritsma, I.T.M.; Kramer, K.

    2002-01-01

    The sensitivity of forest development to climate change is assessed using a gap model. Process descriptions in the gap model of growth, phenology, and seed production were adjusted for climate change effects using a detailed process-based growth modeland a regression analysis. Simulation runs over

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Homoclinic connections and subcritical Neimark bifurcation in a duopoly model with adaptively adjusted productions

    Energy Technology Data Exchange (ETDEWEB)

    Agliari, Anna [Dipartimento di Scienze Economiche e Sociali, Universita Cattolica del Sacro Cuore, Via Emilia Parmense, 84, 29100 Piacenza (Italy)]. E-mail: anna.agliari@unicatt.it

    2006-08-15

    In this paper we study some global bifurcations arising in the Puu's oligopoly model when we assume that the producers do not adjust to the best reply but use an adaptive process to obtain at each step the new production. Such bifurcations cause the appearance of a pair of closed invariant curves, one attracting and one repelling, this latter being involved in the subcritical Neimark bifurcation of the Cournot equilibrium point. The aim of the paper is to highlight the relationship between the global bifurcations causing the appearance/disappearance of two invariant closed curves and the homoclinic connections of some saddle cycle, already conjectured in [Agliari A, Gardini L, Puu T. Some global bifurcations related to the appearance of closed invariant curves. Comput Math Simul 2005;68:201-19]. We refine the results obtained in such a paper, showing that the appearance/disappearance of closed invariant curves is not necessarily related to the existence of an attracting cycle. The characterization of the periodicity tongues (i.e. a region of the parameter space in which an attracting cycle exists) associated with a subcritical Neimark bifurcation is also discussed.

  10. Modeling the Performance of a New Speed Adjustable Compound Supercharging Diesel Engine Working under Plateau Conditions

    Directory of Open Access Journals (Sweden)

    Meng Xia

    2017-05-01

    Full Text Available In order to improve the diesel engine performance under plateau (high altitude conditions, a new Speed Adjustable Compound (SAC supercharging method is proposed. A simulation model based on a six-cylinder V-type turbocharged intercooler diesel engine is built on the GT-POWER platform, and then simulation-based research is carried out. A genetic algorithm (GA is used to identify the best operation parameters, including the supercharger speed and fuel injection quantity under steady state conditions. Transient performance is obtained through starting process simulation of a vehicle with SAC engine on the MATLAB/Simulink GT-POWER co-simulation platform. Both the steady and transient performance of the SAC engine are compared with those of the original engine. Results show that the torque of the SAC engine at full load is significantly increased when the engine speed n < 1600 r/min. The increment of the maximum torque can reach up to 31% at 1000 r/min compared to that of the original engine, while the peak torque is increased by 9%. The fuel consumption deterioration is restricted within 5%. What’s more, the SAC engine can help reducing the acceleration time by 20% during tip-in pedal events during the vehicle starting process.

  11. 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 ...

  12. Adjustments of the TaD electron density reconstruction model with GNSS-TEC parameters for operational application purposes

    Directory of Open Access Journals (Sweden)

    Belehaki Anna

    2012-12-01

    Full Text Available Validation results on the latest version of TaD model (TaDv2 show realistic reconstruction of the electron density profiles (EDPs with an average error of 3 TECU, similar to the error obtained from GNSS-TEC calculated paremeters. The work presented here has the aim to further improve the accuracy of the TaD topside reconstruction, adjusting the TEC parameter calculated from TaD model with the TEC parameter calculated by GNSS transmitting RINEX files provided by receivers co-located with the Digisondes. The performance of the new version is tested during a storm period demonstrating further improvements in respect to the previous version. Statistical comparison of modeled and observed TEC confirms the validity of the proposed adjustment. A significant benefit of the proposed upgrade is that it facilitates the real-time implementation of TaD. The model needs a reliable measure of the scale height at the peak height, which is supposed to be provided by Digisondes. Oftenly, the automatic scaling software fails to correctly calculate the scale height at the peak, Hm, due to interferences in the receiving signal. Consequently the model estimated topside scale height is wrongly calculated leading to unrealistic results for the modeled EDP. The proposed TEC adjustment forces the model to correctly reproduce the topside scale height, despite the inaccurate values of Hm. This adjustment is very important for the application of TaD in an operational environment.

  13. 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.

  14. 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.

  15. [Structural adjustment, cultural adjustment?].

    Science.gov (United States)

    Dujardin, B; Dujardin, M; Hermans, I

    2003-12-01

    Over the last two decades, multiple studies have been conducted and many articles published about Structural Adjustment Programmes (SAPs). These studies mainly describe the characteristics of SAPs and analyse their economic consequences as well as their effects upon a variety of sectors: health, education, agriculture and environment. However, very few focus on the sociological and cultural effects of SAPs. Following a summary of SAP's content and characteristics, the paper briefly discusses the historical course of SAPs and the different critiques which have been made. The cultural consequences of SAPs are introduced and are described on four different levels: political, community, familial, and individual. These levels are analysed through examples from the literature and individual testimonies from people in the Southern Hemisphere. The paper concludes that SAPs, alongside economic globalisation processes, are responsible for an acute breakdown of social and cultural structures in societies in the South. It should be a priority, not only to better understand the situation and its determining factors, but also to intervene and act with strategies that support and reinvest in the social and cultural sectors, which is vital in order to allow for individuals and communities in the South to strengthen their autonomy and identify.

  16. 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.

  17. 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.

  18. Effect of the spray volume adjustment model on the efficiency of fungicides and residues in processing tomato

    Energy Technology Data Exchange (ETDEWEB)

    Ratajkiewicz, H.; Kierzek, R.; Raczkowski, M.; Hołodyńska-Kulas, A.; Łacka, A.; Wójtowicz, A.; Wachowiak, M.

    2016-11-01

    This study compared the effects of a proportionate spray volume (PSV) adjustment model and a fixed model (300 L/ha) on the infestation of processing tomato with potato late blight (Phytophthora infestans (Mont.) de Bary) (PLB) and azoxystrobin and chlorothalonil residues in fruits in three consecutive seasons. The fungicides were applied in alternating system with or without two spreader adjuvants. The proportionate spray volume adjustment model was based on the number of leaves on plants and spray volume index. The modified Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method was optimized and validated for extraction of azoxystrobin and chlorothalonil residue. Gas chromatography with a nitrogen and phosphorus detector and an electron capture detector were used for the analysis of fungicides. The results showed that higher fungicidal residues were connected with lower infestation of tomato with PLB. PSV adjustment model resulted in lower infestation of tomato than the fixed model (300 L/ha) when fungicides were applied at half the dose without adjuvants. Higher expected spray interception into the tomato canopy with the PSV system was recognized as the reasons of better control of PLB. The spreader adjuvants did not have positive effect on the biological efficacy of spray volume application systems. The results suggest that PSV adjustment model can be used to determine the spray volume for fungicide application for processing tomato crop. (Author)

  19. Effect of the spray volume adjustment model on the efficiency of fungicides and residues in processing tomato

    Directory of Open Access Journals (Sweden)

    Henryk Ratajkiewicz

    2016-08-01

    Full Text Available This study compared the effects of a proportionate spray volume (PSV adjustment model and a fixed model (300 L/ha on the infestation of processing tomato with potato late blight (Phytophthora infestans (Mont. de Bary (PLB and azoxystrobin and chlorothalonil residues in fruits in three consecutive seasons. The fungicides were applied in alternating system with or without two spreader adjuvants. The proportionate spray volume adjustment model was based on the number of leaves on plants and spray volume index. The modified Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS method was optimized and validated for extraction of azoxystrobin and chlorothalonil residue. Gas chromatography with a nitrogen and phosphorus detector and an electron capture detector were used for the analysis of fungicides. The results showed that higher fungicidal residues were connected with lower infestation of tomato with PLB. PSV adjustment model resulted in lower infestation of tomato than the fixed model (300 L/ha when fungicides were applied at half the dose without adjuvants. Higher expected spray interception into the tomato canopy with the PSV system was recognized as the reasons of better control of PLB. The spreader adjuvants did not have positive effect on the biological efficacy of spray volume application systems. The results suggest that PSV adjustment model can be used to determine the spray volume for fungicide application for processing tomato crop.

  20. [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.

  1. 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...

  2. 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)

  3. 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.

  4. 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

  5. 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

  6. Rejection, feeling bad, and being hurt: using multilevel modeling to clarify the link between peer group aggression and adjustment.

    Science.gov (United States)

    Rulison, Kelly L; Gest, Scott D; Loken, Eric; Welsh, Janet A

    2010-12-01

    The association between affiliating with aggressive peers and behavioral, social and psychological adjustment was examined. Students initially in 3rd, 4th, and 5th grade (N = 427) were followed biannually through 7th grade. Students' peer-nominated groups were identified. Multilevel modeling was used to examine the independent contributions of adolescents' typical peer context (between-person effect) and changes in peer context (within-person effects) to adolescents' adjustment. Typically affiliating with aggressive groups and affiliating with more aggressive groups than usual predicted higher aggression for all youth. Typically affiliating with aggressive groups predicted negative adjustment (lower social preference and self-worth, higher victimization) for girls but neutral or positive adjustment for boys. Although typical peer context was consistently associated with adjustment, changes in peer context predicted small changes in adjustment for several outcomes. Results underscored the need to adopt a more differentiated picture of adolescents' dynamic peer context and its association with normative development. Copyright © 2010 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  7. 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

  8. Modeling cognitive adaptation: a longitudinal investigation of the impact of individual differences and coping on college adjustment and performance.

    Science.gov (United States)

    Aspinwall, L G; Taylor, S E

    1992-12-01

    Drawing on cognitive adaptation theory, optimism, psychological control, and self-esteem were explored as longitudinal predictors of adjustment to college in a sample of 672 freshmen. Although a direct effect of optimism on adjustment was found, most of the predicted effects were mediated by coping methods. Controlling for initial positive and negative mood, the beneficial effects of optimism, control, and self-esteem on adjustment were mediated by the nonuse of avoidance coping, greater use of active coping, and greater seeking of social support. Alternative models of the causal relations among these variables did not fit the data as well as the a priori mediational model. The results of a 2-year follow-up indicated that self-esteem and control predicted greater motivation and higher grades, controlling for college entrance exam scores. Implications for cognitive adaptation theory and for interventions with populations under stress are discussed.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. Prognostic model for advanced breast carcinoma with luminal subtype and impact of hormonal maintenance: Implications for post-progression and conditional survival.

    Science.gov (United States)

    Carbognin, Luisa; Sperduti, Isabella; Ciccarese, Mariangela; Fabi, Alessandra; Petrucelli, Luciana; Vari, Sabrina; Forcignanò, Rosa Chiara; Nortilli, Rolando; Vicentini, Cecilia; Pilotto, Sara; Merler, Sara; Zampiva, Ilaria; Brunelli, Matteo; Manfrin, Erminia; Giannarelli, Diana; Tortora, Giampaolo; Bria, Emilio

    2016-10-01

    The aim of this analysis was to develop and validate a prognostic model for advanced breast cancer (ABC) with luminal subtype based on the combination of clinical, pathological and therapeutic predictors to provide a practical tool to evaluate patients' prognosis. Clinical and pathological data were retrospectively correlated to progression-free and overall survival (PFS/OS) using a Cox model. Significant treatment variables were adjusted with the propensity score analysis. A continuous score to identify risk classes was derived according to model ratios. The performance of the risk-class model was tested for post-progression survival (PPS) and conditional survival (CS) as well. Data from 335 patients (3 institutions) were gathered (median follow-up 58 months). At multivariate analysis Ki67, Performance Status (PS) and number of metastatic sites were significant predictors for PFS, whereas Ki67, PS, brain metastases, PFS after 1st-line therapy, number of chemotherapy lines, hormonal therapy and maintenance were significant predictors for OS. The hormonal maintenance resulted to be prognostic after adjustment with propensity score analysis. A two-class model significantly differentiated low-risk and high-risk patients for 2-year PFS (31.5% and 11.0%, p model separated low risk, intermediate-risk, and high-risk patients for 2-year PFS (40.8%, 24.4%, and 11.0%, p models equally discriminate the luminal ABC prognosis in terms of PPS and CS. A risk stratification model including 'easy-to-obtain' clinical, pathological and therapeutic parameters accurately separates luminal ABC patients into different risk classes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. DaMoScope and its internet graphics for the visual control of adjusting mathematical models describing experimental data

    Energy Technology Data Exchange (ETDEWEB)

    Belousov, V. I.; Ezhela, V. V.; Kuyanov, Yu. V., E-mail: Yu.Kuyanov@gmail.com; Tkachenko, N. P. [Institute for High Energy Physics, National Research Center Kurchatov Institute, COMPAS Group (Russian Federation)

    2015-12-15

    The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.

  16. 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...

  17. 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.

  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. 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.

  20. 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...

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Development of a Risk-adjustment Model for the Inpatient Rehabilitation Facility Discharge Self-care Functional Status Quality Measure.

    Science.gov (United States)

    Deutsch, Anne; Pardasaney, Poonam; Iriondo-Perez, Jeniffer; Ingber, Melvin J; Porter, Kristie A; McMullen, Tara

    2017-07-01

    Functional status measures are important patient-centered indicators of inpatient rehabilitation facility (IRF) quality of care. We developed a risk-adjusted self-care functional status measure for the IRF Quality Reporting Program. This paper describes the development and performance of the measure's risk-adjustment model. Our sample included IRF Medicare fee-for-service patients from the Centers for Medicare & Medicaid Services' 2008-2010 Post-Acute Care Payment Reform Demonstration. Data sources included the Continuity Assessment Record and Evaluation Item Set, IRF-Patient Assessment Instrument, and Medicare claims. Self-care scores were based on 7 Continuity Assessment Record and Evaluation items. The model was developed using discharge self-care score as the dependent variable, and generalized linear modeling with generalized estimation equation to account for patient characteristics and clustering within IRFs. Patient demographics, clinical characteristics at IRF admission, and clinical characteristics related to the recent hospitalization were tested as risk adjusters. A total of 4769 patient stays from 38 IRFs were included. Approximately 57% of the sample was female; 38.4%, 75-84 years; and 31.0%, 65-74 years. The final model, containing 77 risk adjusters, explained 53.7% of variance in discharge self-care scores (P<0.0001). Admission self-care function was the strongest predictor, followed by admission cognitive function and IRF primary diagnosis group. The range of expected and observed scores overlapped very well, with little bias across the range of predicted self-care functioning. Our risk-adjustment model demonstrated strong validity for predicting discharge self-care scores. Although the model needs validation with national data, it represents an important first step in evaluation of IRF functional outcomes.

  7. 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.

  8. Rejection, Feeling Bad, and Being Hurt: Using Multilevel Modeling to Clarify the Link between Peer Group Aggression and Adjustment

    Science.gov (United States)

    Rulison, Kelly L.; Gest, Scott D.; Loken, Eric; Welsh, Janet A.

    2010-01-01

    The association between affiliating with aggressive peers and behavioral, social and psychological adjustment was examined. Students initially in 3rd, 4th, and 5th grade (N = 427) were followed biannually through 7th grade. Students' peer-nominated groups were identified. Multilevel modeling was used to examine the independent contributions of…

  9. Functional form and risk adjustment of hospital costs: Bayesian analysis of a Box-Cox random coefficients model.

    Science.gov (United States)

    Hollenbeak, Christopher S

    2005-10-15

    While risk-adjusted outcomes are often used to compare the performance of hospitals and physicians, the most appropriate functional form for the risk adjustment process is not always obvious for continuous outcomes such as costs. Semi-log models are used most often to correct skewness in cost data, but there has been limited research to determine whether the log transformation is sufficient or whether another transformation is more appropriate. This study explores the most appropriate functional form for risk-adjusting the cost of coronary artery bypass graft (CABG) surgery. Data included patients undergoing CABG surgery at four hospitals in the midwest and were fit to a Box-Cox model with random coefficients (BCRC) using Markov chain Monte Carlo methods. Marginal likelihoods and Bayes factors were computed to perform model comparison of alternative model specifications. Rankings of hospital performance were created from the simulation output and the rankings produced by Bayesian estimates were compared to rankings produced by standard models fit using classical methods. Results suggest that, for these data, the most appropriate functional form is not logarithmic, but corresponds to a Box-Cox transformation of -1. Furthermore, Bayes factors overwhelmingly rejected the natural log transformation. However, the hospital ranking induced by the BCRC model was not different from the ranking produced by maximum likelihood estimates of either the linear or semi-log model. Copyright (c) 2005 John Wiley & Sons, Ltd.

  10. Predictive value of the age-adjusted Charlston co-morbidity index on peri-operative complications, adjuvant chemotherapy usage and survival in patients undergoing debulking surgery after neo-adjuvant chemotherapy for advanced epithelial ovarian cancer.

    Science.gov (United States)

    Phillips, Andrew; Singh, Kavita; Pounds, Rachel; Sundar, Sudha; Kehoe, Sean; Nevin, James; Elattar, Ahmed; Balega, Janos

    2017-11-01

    The aim of this study was to determine whether the age-adjusted Charlston co-morbidity index (ACCI) can predict post-operative complications, adjuvant chemotherapy usage and overall survival (OS) in patients with advanced epithelial ovarian cancer (AOC) treated with neoadjuvant chemotherapy (NACT). A review was performed of all cytoreductive surgeries performed between 16/8/07-3/2/14 for AOC at a UK Cancer Centre. All surgeries were stratified by ACCI into three groups: Low (0-1), Intermediate (2-3) and High (≥4). Of the 293 cases the ACCI distribution was: 74 (25.26%) low, 164 (55.97%) intermediate and 55 (18.77%) high. Patients with a high ACCI were less likely to receive adjuvant chemotherapy (p = .023), more likely to receive fewer adjuvant cycles (p = .0057) but no more likely to experience complications. Median OS for patients with a low, intermediate and high ACCI was 44.58 (95%CI 36.98-52.19), 34.65 (95%CI 29.48-39.82) and 33.37 (95%CI 17.47-49.27) months. ACCI was associated with OS (p Co-morbidity Index has previously been identified as a predictor of survival in both medical and surgical conditions. Recently it has also been validated in patients undergoing primary cytoreductive surgery for advanced ovarian cancer. This study is the first to validate the Age-Adjusted Charlston Co-morbidity Index in patients undergoing cytoreductive surgery following neoadjuvant chemotherapy. Our findings demonstrate that it can be used to not only predict overall survival in women undergoing debulking surgery after neo-adjuvant chemotherapy but also predicts the uptake and commencement of adjuvant chemotherapy. Such findings are important considerations to enable an informed patient choice regarding interval surgery in the more co-morbid patients. More importantly, although the ACCI can be used as a marker of overall survival, even in the most co-morbid of patients there remains a significant survival advantage following surgery to the extent that it should

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    We extend the VAR based intertemporal asset allocation approach from Campbell et al. (2003) to the case where the VAR parameter estimates are adjusted for small- sample bias. We apply the analytical bias formula from Pope (1990) using both Campbell et al.'s dataset, and an extended dataset...

  17. 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.

  18. 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

  19. Analysis of case-parent trios for imprinting effect using a loglinear model with adjustment for sex-of-parent-specific transmission ratio distortion

    DEFF Research Database (Denmark)

    Huang, Lam Opal; Infante-Rivard, Claire; Labbe, Aurélie

    2017-01-01

    of the minor allele in control-trios can be added to the loglinear model to adjust for TRD. Adjusting the model removes the inflation in the genotype relative risk (RR) estimate and Type 1 error introduced by non-sex-of-parent-specific TRD. We now propose to further extend this model to estimate an imprinting...... propose a sex-of-parent-specific transmission offset in adjusting the loglinear model to account for ST. This extended model restores the correct RR estimates for child and imprinting effects, adjusts for inflation in Type 1 error, and improves performance on sensitivity and specificity compared...

  20. Convexity Adjustments

    DEFF Research Database (Denmark)

    M. Gaspar, Raquel; Murgoci, Agatha

    2010-01-01

    A convexity adjustment (or convexity correction) in fixed income markets arises when one uses prices of standard (plain vanilla) products plus an adjustment to price nonstandard products. We explain the basic and appealing idea behind the use of convexity adjustments and focus on the situations o...

  1. O dia seguinte: as credenciais da sobrevivência ao ajuste nas empresas The day after: the surviving credentials to the enterprises adjustment

    Directory of Open Access Journals (Sweden)

    Nadya Araujo Guimarães

    1999-12-01

    Full Text Available O artigo tem por objetivo refletir sobre os nexos entre seletividade ocupacional e requerimentos de qualificação numa conjuntura de intensa reestruturação industrial. Para tanto, procura responder duas questões. Primeira: quem tem sobrevivido ao ajuste ocorrido no mercado de trabalho industrial no Brasil dos anos 90? Isto é, que credenciais de qualificação permitiram tal sobrevivência? Segunda: qual o tipo de inserção oferecida aos que lograram permanecer integrados a estas indústrias? Isto é, em que tipo de postos de trabalho sobreviveram tais trabalhadores remanescentes? Para ilustrar a análise, toma-se em consideração um segmento especial - a indústria química - exemplar pelas intensas mudanças que sofreu nos anos 90 e também pela prévia e elevada seletividade de seus critérios de recrutamento. A análise empírica se baseia em estatísticas do Ministério do Trabalho (Rais-Caged.The article analyses the relationship between occupational selectivity and skill requirements under conditions of intense industrial restructuring. It is organized in two parts. In the first one, it analyses the profile of employees who survived the severe downsizing process in Brazilian industry during the 90's. In its second part, the article analyses the profile of the remaining job positions. Chemical industry is taken as empirical reference, partly for the intensity of technological, organizational and regulatory changes it exemplifies, and partly for its previous characteristic of high selectivity in terms of age, sex and schooling levels of its working force. Data basis are administrative informations on employment in Brazilian formal sector produced by Ministry of Labor (Rais-Caged

  2. Filling Gaps in the Acculturation Gap-Distress Model: Heritage Cultural Maintenance and Adjustment in Mexican-American Families.

    Science.gov (United States)

    Telzer, Eva H; Yuen, Cynthia; Gonzales, Nancy; Fuligni, Andrew J

    2016-07-01

    The acculturation gap-distress model purports that immigrant children acculturate faster than do their parents, resulting in an acculturation gap that leads to family and youth maladjustment. However, empirical support for the acculturation gap-distress model has been inconclusive. In the current study, 428 Mexican-American adolescents (50.2 % female) and their primary caregivers independently completed questionnaires assessing their levels of American and Mexican cultural orientation, family functioning, and youth adjustment. Contrary to the acculturation gap-distress model, acculturation gaps were not associated with poorer family or youth functioning. Rather, adolescents with higher levels of Mexican cultural orientations showed positive outcomes, regardless of their parents' orientations to either American or Mexican cultures. Findings suggest that youths' heritage cultural maintenance may be most important for their adjustment.

  3. 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

  4. 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

  5. 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

  6. 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-...

  7. Blood lactate levels differ significantly between surviving and nonsurviving patients within the same risk-adjusted Classification for Congenital Heart Surgery (RACHS-1) group after pediatric cardiac surgery.

    Science.gov (United States)

    Molina Hazan, Vered; Gonen, Yael; Vardi, Amir; Keidan, Ilan; Mishali, David; Rubinshtein, Marina; Yakov, Yusim; Paret, Gideon

    2010-10-01

    This study aimed to examine the association between lactate levels in the first hours after surgery for congenital heart defects and the results of Risk-Adjusted Classification for Congenital Heart Surgery (RACHS-1) scoring and to evaluate serial lactate levels over time to determine whether they can serve as a supplementary tool for postoperative assessment within the same RACHS-1 group of patients. A retrospective cohort study was performed using data retrieved from a clinical database of 255 children who had surgery for congenital heart defects between 1999 and 2001 at Sheba Medical Center. Lactate levels were measured postoperatively four times (mg/dL units). The last sample was taken at the end of the surgical procedure, and lactate levels were measured at admission to the pediatrics critical care unit, then 6 and 12 h after admission. The lactate level was measured via arterial blood gases. A total of 27 deaths occurred, yielding a mortality rate of 7.4% when Norwood operations were excluded and 10.16% when they were included. The mean initial postoperative lactate level was significantly lower for survivors (42.2 ± 32.0 mg/dL) than for nonsurvivors (85.4 ± 54.1 mg/dL) (p 0.96 for all). The Pearson correlations between postoperative lactate levels (last lactate measurement taken in the operating room) and cardiopulmonary bypass (CPB) duration (r = 0.549), clamp duration (r = 0.586), and the inotropic score (r = 0.466) (p maximum lactate levels (during the first 12 postoperative hours) and CPB duration (r = 0.496), clamp duration (r = 0.509), and the inotropic score (r = 0.633) (p < 0.001 for all) were extremely positive. The early elevation of lactate levels in RACHS-1 subgroups 1 to 3 were highly correlated with poor prognosis and death (p < 0.03). In addition, the lactate levels differed significantly between survivors and nonsurvivors within the same RACHS-1 subgroup. The survivors in RACHS-1 subgroups 1 to 3 had lower mean lactate levels than the

  8. 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.

  9. 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

  10. Avoiding radical surgery improves early survival in elderly patients with rectal cancer, demonstrating complete clinical response after neoadjuvant therapy: results of a decision-analytic model.

    Science.gov (United States)

    Smith, Fraser McLean; Rao, Christopher; Oliva Perez, Rodrigo; Bujko, Krzysztof; Athanasiou, Thanos; Habr-Gama, Angelita; Faiz, Omar

    2015-02-01

    In elderly and comorbid patients with rectal cancer, radical surgery is associated with significant perioperative mortality. Data suggest that a watch-and-wait approach where a complete clinical response is obtained after neoadjuvant chemoradiotherapy might be oncologically safe. This study aimed to determine whether patient age and comorbidity should influence surgeon and patient decision making where a complete clinical response is obtained. Decision-analytic modeling consisting of a decision tree and Markov chain simulation was used. Modeled outcome parameters were elicited both from comprehensive literature review and from a national patient outcomes database. Outcomes for 3 patient cohorts treated with neoadjuvant therapy were modeled after either surgery or watch and wait. Patients included 60-year-old and 80-year-old men with mild comorbidities (Charlson score 3). Absolute survival, disease-free survival, and quality-adjusted life years were measured. The model found that absolute survival was similar in 60-year-old patients but was significantly improved in fit and comorbid 80-year-old patients at 1 year after treatment where watch and wait was implemented instead of radical surgery, with a survival advantage of 10.1% (95% CI, 7.9-12.6) and 13.5% (95% CI, 10.2-16.9). At all of the other time points, absolute survival was equivalent for both techniques. There were no short- or long-term differences among any patient groups managed either by radical surgery or watch and wait in terms of either disease-free survival or quality-adjusted life years. Oncologic data for the watch-and-wait approach used for this study is derived from only a small number of studies pertaining to a highly selected group of patients. The 90-day postoperative mortality rate derived from the United Kingdom population-based study might be lower in other countries or individual institutions. This study suggests competing effects of oncologic and surgical risk when using watch

  11. Optimized ventricular restraint therapy: adjustable restraint is superior to standard restraint in an ovine model of ischemic cardiomyopathy.

    Science.gov (United States)

    Lee, Lawrence S; Ghanta, Ravi K; Mokashi, Suyog A; Coelho-Filho, Otavio; Kwong, Raymond Y; Kwon, Michael; Guan, Jian; Liao, Ronglih; Chen, Frederick Y

    2013-03-01

    The effects of ventricular restraint level on left ventricular reverse remodeling are not known. We hypothesized that restraint level affects the degree of reverse remodeling and that restraint applied in an adjustable manner is superior to standard, nonadjustable restraint. This study was performed in 2 parts using a model of chronic heart failure in the sheep. In part I, restraint was applied at control (0 mm Hg, n = 3), low (1.5 mm Hg, n = 3), and high (3.0 mm Hg, n = 3) levels with an adjustable and measurable ventricular restraint (AMVR) device. Restraint level was not altered throughout the 2-month treatment period. Serial restraint level measurements and transthoracic echocardiography were performed. In part II, restraint was applied with the AMVR device set at 3.0 mm Hg (n = 6) and adjusted periodically to maintain that level. This was compared with restraint applied in a standard, nonadjustable manner using a mesh wrap (n = 6). All subjects were followed up for 2 months with serial magnetic resonance imaging. In part I, there was greater and earlier reverse remodeling in the high restraint group. In both groups, the rate of reverse remodeling peaked and then declined as the measured restraint level decreased with progression of reverse remodeling. In part II, adjustable restraint resulted in greater reverse remodeling than standard restraint. Left ventricular end diastolic volume decreased by 12.7% (P = .005) with adjustable restraint and by 5.7% (P = .032) with standard restraint. Left ventricular ejection fraction increased by 18.9% (P = .014) and 14.4% (P standard restraint, respectively. Restraint level affects the rate and degree of reverse remodeling and is an important determinant of therapy efficacy. Adjustable restraint is more effective than nonadjustable restraint in promoting reverse remodeling. Copyright © 2013 The American Association for Thoracic Surgery. Published by Mosby, Inc. All rights reserved.

  12. Optimized ventricular restraint therapy: Adjustable restraint is superior to standard restraint in an ovine model of ischemic cardiomyopathy

    Science.gov (United States)

    Lee, Lawrence S.; Ghanta, Ravi K.; Mokashi, Suyog A.; Coelho-Filho, Otavio; Kwong, Raymond Y.; Kwon, Michael; Guan, Jian; Liao, Ronglih; Chen, Frederick Y.

    2014-01-01

    Objective The effects of ventricular restraint level on left ventricular reverse remodeling are not known. We hypothesized that restraint level affects the degree of reverse remodeling and that restraint applied in an adjustable manner is superior to standard, nonadjustable restraint. Methods This study was performed in 2 parts using a model of chronic heart failure in the sheep. In part I, restraint was applied at control (0 mm Hg, n = 3), low (1.5 mm Hg, n = 3), and high (3.0 mm Hg, n = 3) levels with an adjustable and measurable ventricular restraint (AMVR) device. Restraint level was not altered throughout the 2-month treatment period. Serial restraint level measurements and transthoracic echocardiography were performed. In part II, restraint was applied with the AMVR device set at 3.0 mm Hg (n = 6) and adjusted periodically to maintain that level. This was compared with restraint applied in a standard, nonadjustable manner using a mesh wrap (n = 6). All subjects were followed up for 2 months with serial magnetic resonance imaging. Results In part I, there was greater and earlier reverse remodeling in the high restraint group. In both groups, the rate of reverse remodeling peaked and then declined as the measured restraint level decreased with progression of reverse remodeling. In part II, adjustable restraint resulted in greater reverse remodeling than standard restraint. Left ventricular end diastolic volume decreased by 12.7% (P = .005) with adjustable restraint and by 5.7% (P = .032) with standard restraint. Left ventricular ejection fraction increased by 18.9% (P = .014) and 14.4% (Prestraint, respectively. Conclusions Restraint level affects the rate and degree of reverse remodeling and is an important determinant of therapy efficacy. Adjustable restraint is more effective than nonadjustable restraint in promoting reverse remodeling. PMID:22698557

  13. Global and regional estimates of cancer mortality and incidence by site: I. Application of regional cancer survival model to estimate cancer mortality distribution by site

    Directory of Open Access Journals (Sweden)

    Lopez Alan D

    2002-12-01

    Full Text Available Abstract Background The Global Burden of Disease 2000 (GBD 2000 study starts from an analysis of the overall mortality envelope in order to ensure that the cause-specific estimates add to the total all cause mortality by age and sex. For regions where information on the distribution of cancer deaths is not available, a site-specific survival model was developed to estimate the distribution of cancer deaths by site. Methods An age-period-cohort model of cancer survival was developed based on data from the Surveillance, Epidemiology, and End Results (SEER. The model was further adjusted for the level of economic development in each region. Combined with the available incidence data, cancer death distributions were estimated and the model estimates were validated against vital registration data from regions other than the United States. Results Comparison with cancer mortality distribution from vital registration confirmed the validity of this approach. The model also yielded the cancer mortality distribution which is consistent with the estimates based on regional cancer registries. There was a significant variation in relative interval survival across regions, in particular for cancers of bladder, breast, melanoma of the skin, prostate and haematological malignancies. Moderate variations were observed among cancers of colon, rectum, and uterus. Cancers with very poor prognosis such as liver, lung, and pancreas cancers showed very small variations across the regions. Conclusions The survival model presented here offers a new approach to the calculation of the distribution of deaths for areas where mortality data are either scarce or unavailable.

  14. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    Energy Technology Data Exchange (ETDEWEB)

    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.

  15. 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.

  16. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

    Energy Technology Data Exchange (ETDEWEB)

    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.

  17. 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.

  18. Mathematical modelling of survival of glioblastoma patients suggests a role for radiotherapy dose escalation and predicts poorer outcome after delay to start treatment.

    Science.gov (United States)

    Burnet, N G; Jena, R; Jefferies, S J; Stenning, S P; Kirkby, N F

    2006-03-01

    The outcome of patients with glioblastoma (GBM) remains extremely poor. We have developed a mathematical model, using pathological and radiation biology concepts, to assess the detrimental effect of delay to start radiotherapy, the possible benefit from dose escalation, and to extract biological data from clinical data. Survival data were available for 154 adult patients with GBM treated in our centre with curative intent to a dose of 60 Gy in 30 fractions between 1996 and 2002. Survival data for 129 patients from the 60 Gy arm of the MRC BR02 randomised trial of radiotherapy dose were obtained for comparison. The model generates the equivalent of individual patients with a brain tumour, and produces an explicit outcome, either death or survival. The tumour, assumed to be growing exponentially, causes normal cell damage in the brain, and death occurs when the number of normal brain cells falls below a critical level. The outcome for an individual patient is determined by values of the variables assigned by the model. Parameters for the single patient include tumour doubling time, surviving fraction of tumour cells after each fraction of radiotherapy, and a waiting time from presentation to the start of radiotherapy. A surrogate for performance status is implemented, using a rule that rejects patients whose tumours are too advanced at presentation to be suitable for radical radiotherapy. Values for the parameters that determine individual patient outcome are randomly assigned from a set of probability distributions, using Monte Carlo simulation. The simulation constructs survival results for a population, typically 2000 individuals. The descriptors of the probability distributions that are used to determine the parameters that define the patient characteristics are adjusted to optimise the fit of the modelled population to real clinical data, using a combination of folding polygon and simulated annealing techniques. The model fits the clinical data well. The results

  19. 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

  20. 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.

  1. Analytical Model of Cyclic Heat Exchange of the Plate of Finite Sizes Adjusted for the Thermal Relaxation

    Science.gov (United States)

    Kirsanov, Yu A.; Makarushkin, D. V.; Yudakhin, A. E.; Kirsanov, A. Yu

    2017-11-01

    The hyperbolic boundary value problem of heat conduction in a two-dimensional rectangular plate with the third kind boundary conditions was formulated. The model of transient thermal processes in the body takes into account changes in time and along the flow direction of the ambient temperature. An analytical solution was obtained for the temperature field in the plate, adjusted for the phenomena of thermal relaxation and thermal damping.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Multiple adjustable vascular clamp prototype: feasibility study on an experimental model of end-to-side microsurgical vascular anastomosis.

    Science.gov (United States)

    Pereira, A; Ichihara, S; Collon, S; Bodin, F; Gay, A; Facca, S; Liverneaux, P

    2014-12-01

    The aim of this study was to establish the feasibility of microsurgical end-to-side vascular anastomosis with a multiclamp adjustable vascular clamp prototype in an inert experimental model. Our method consisted of performing an end-to-side microsurgical anastomosis with 10/0 suture on a 2-mm diameter segment. In group 1, the end-to-side segment was held in place by a double clamp and a single end clamp. In group 2, the segment was held in place with a single multiclamp adjustable clamp. The average time for performing the anastomosis was shorter in group 2. The average number of sutures was the same in both groups. No leak was found and permeability was always positive in both groups. Our results show that performing end-to-side anastomosis with a multiclamp adjustable vascular clamp is feasible in an inert experimental model. Feasibility in a live animal model has to be demonstrated before clinical use. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  7. 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.

  8. Overall survival in lower IPSS risk MDS by receipt of iron chelation therapy, adjusting for patient-related factors and measuring from time of first red blood cell transfusion dependence: an MDS-CAN analysis.

    Science.gov (United States)

    Leitch, Heather A; Parmar, Ambica; Wells, Richard A; Chodirker, Lisa; Zhu, Nancy; Nevill, Thomas J; Yee, Karen W L; Leber, Brian; Keating, Mary-Margaret; Sabloff, Mitchell; St Hilaire, Eve; Kumar, Rajat; Delage, Robert; Geddes, Michelle; Storring, John M; Kew, Andrea; Shamy, April; Elemary, Mohamed; Lenis, Martha; Mamedov, Alexandre; Ivo, Jessica; Francis, Janika; Zhang, Liying; Buckstein, Rena

    2017-10-01

    Analyses suggest iron overload in red blood cell (RBC) transfusion-dependent (TD) patients with myleodysplastic syndrome (MDS) portends inferior overall survival (OS) that is attenuated by iron chelation therapy (ICT) but may be biassed by unbalanced patient-related factors. The Canadian MDS Registry prospectively measures frailty, comorbidity and disability. We analysed OS by receipt of ICT, adjusting for these patient-related factors. TD International Prognostic Scoring System (IPSS) low and intermediate-1 risk MDS, at RBC TD, were included. Predictive factors for OS were determined. A matched pair analysis considering age, revised IPSS, TD severity, time from MDS diagnosis to TD, and receipt of disease-modifying agents was conducted. Of 239 patients, 83 received ICT; frailty, comorbidity and disability did not differ from non-ICT patients. Median OS from TD was superior in ICT patients (5·2 vs. 2·1 years; P MDS, adjusting for age, frailty, comorbidity, disability, revised IPSS, TD severity, time to TD and receiving disease-modifying agents. This provides additional evidence that ICT may confer clinical benefit. © 2017 John Wiley & Sons Ltd.

  9. 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.

  10. Siblings’ Perceptions of Differential Treatment, Fairness, and Jealousy and Adolescent Adjustment: A Moderated Indirect Effects Model

    Science.gov (United States)

    Loeser, Meghan K.; Whiteman, Shawn D.; McHale, Susan M.

    2016-01-01

    Youth's perception of parents’ differential treatment (PDT) are associated with maladjustment during adolescence. Although the direct relations between PDT and youth's maladjustment have been well established, the mechanisms underlying these associations remain unclear. We addressed this gap by examining whether sibling jealousy accounted for the links between PDT and youth's depressive symptoms, self-worth, and risky behaviors. Additionally, we examined whether youth's perceptions of fairness regarding their treatment as well as the gender constellation of the dyad moderated these indirect relations (i.e., moderated-indirect effects). Participants were first- and second-born adolescent siblings (M = 15.96, SD = .72 years for older siblings, M = 13.48, SD = 1.02 years for younger siblings) and their parents from 197 working and middle class European American families. Data were collected via home interviews. A series of Conditional Process Analyses revealed significant indirect effects of PDT through sibling jealousy to all three adjustment outcomes. Furthermore, perceptions of fairness moderated the relations between PDT and jealousy, such that the indirect effects were only significant at low (−1 SD) and average levels of fairness. At high levels of fairness (+1 SD) there was no association between PDT, jealousy, and youth adjustment. Taken together, results indicate that youth and parents would benefit from engaging in clear communication regarding the reasoning for the occurrence of differential treatment, likely maximizing youth and parent perceptions of that treatment as being fair, and in turn mitigating sibling jealousy and maladjustment. PMID:27867295

  11. Siblings' Perceptions of Differential Treatment, Fairness, and Jealousy and Adolescent Adjustment: A Moderated Indirect Effects Model.

    Science.gov (United States)

    Loeser, Meghan K; Whiteman, Shawn D; McHale, Susan M

    2016-08-01

    Youth's perception of parents' differential treatment (PDT) are associated with maladjustment during adolescence. Although the direct relations between PDT and youth's maladjustment have been well established, the mechanisms underlying these associations remain unclear. We addressed this gap by examining whether sibling jealousy accounted for the links between PDT and youth's depressive symptoms, self-worth, and risky behaviors. Additionally, we examined whether youth's perceptions of fairness regarding their treatment as well as the gender constellation of the dyad moderated these indirect relations (i.e., moderated-indirect effects). Participants were first- and second-born adolescent siblings (M = 15.96, SD = .72 years for older siblings, M = 13.48, SD = 1.02 years for younger siblings) and their parents from 197 working and middle class European American families. Data were collected via home interviews. A series of Conditional Process Analyses revealed significant indirect effects of PDT through sibling jealousy to all three adjustment outcomes. Furthermore, perceptions of fairness moderated the relations between PDT and jealousy, such that the indirect effects were only significant at low (-1 SD) and average levels of fairness. At high levels of fairness (+1 SD) there was no association between PDT, jealousy, and youth adjustment. Taken together, results indicate that youth and parents would benefit from engaging in clear communication regarding the reasoning for the occurrence of differential treatment, likely maximizing youth and parent perceptions of that treatment as being fair, and in turn mitigating sibling jealousy and maladjustment.

  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. 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.

  16. 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.

  17. 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.

  18. COMPASS: a novel risk-adjustment model for catheter ablation in pediatric and congenital heart disease patients.

    Science.gov (United States)

    Triedman, John K; Pfeiffer, Patricia; Berman, Adam; Blaufox, Andrew D; Cannon, Bryan C; Fish, Frank A; Perry, James; Pflaumer, Andreas; Seslar, Stephen P

    2013-01-01

    Robust risk-adjustment algorithms are often necessary if data from clinical registries is to be used to compare rates of important clinical outcomes between participating centers. Although such algorithms have been successfully developed for surgical and catheter-based cardiac interventions in children, outcomes of pediatric and congenital catheter ablation have not been modeled with respect to case mix. A working group was appointed by the Pediatric and Congenital Electrophysiology Society to develop a risk-adjustment algorithm for use in conjunction with a modernized, multicenter registry database. Expert consensus was used to develop relevant outcome measures, an inclusive list of possible predictors, and estimates of associated incremental risk. Historical data from the Pediatric Radiofrequency Ablation Registry was reanalyzed using multivariate regression to create statistical models of ablation outcomes. Acute ablation failure and serious adverse event rates were modeled as outcomes. Statistical modeling was performed on 4486 cases performed in 19 centers. For ablation failure rate, a simple model including general category of arrhythmia mechanism and presence of structural congenital heart disease accounted for ∼71% of outcome variance. The model was useful for identification of between-center variability in the historical data set. Although expert consensus predicted the need for a more complex model, predicted univariate effects were similar to those generated by statistical modeling. Serious adverse events were too infrequent to permit statistical association with any predictive variable, but could be compared with the mean rate observed among all centers. A substantial component of the intercenter variability of acute ablation outcomes in a historical database of pediatric and congenital ablation patients may be accounted for by a simple statistical model, exposing variations in outcome specific to centers. This will be a useful initial model for use a

  19. 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.

  20. 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)

  1. 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.

  2. 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

  3. Statistical Model Based HPLC Analytical Method Adjustment Strategy to Adapt to Different Sets of Analytes in Complicated Samples.

    Science.gov (United States)

    Yan, Binjun; Bai, Xue; Sheng, Yunjie; Li, Fanzhu

    2017-09-01

    On account of the complicated compositions of the products like traditional Chinese medicines (TCMs) and functional foods, it is a common practice to determine different sets of analytes in the same product for different purposes. To efficiently develop the corresponding HPLC methods, a statistical model based analytical method adjustment (SMB-AMA) strategy was proposed. In this strategy, the HPLC data acquired with design of experiments methodology were efficiently utilised to build the retention models for all the analytes and interferences shown in the chromatograms with multivariate statistical modelling methods. According to the set of analytes under research, Monte-Carlo simulations were conducted based on these retention models to estimate the probability of achieving adequate separations between all the analytes and their interferences. Then the analytical parameters were mathematically optimised to the point giving a high value of this probability to compose a robust HPLC method. Radix Angelica Sinensis (RAS) and its TCM formula with Folium Epimedii (FE) were taken as the complicated samples for case studies. The retention models for the compounds in RAS and FE were built independently with correlation coefficients all above 0.9799. The analytical parameters were tactfully adjusted to adapt to six cases of different sets of analytes and different sample matrices. In the validation experiments using the adjusted analytical parameters, satisfactory separations were acquired. The results demonstrated that the SMB-AMA strategy was able to develop HPLC methods rationally and rapidly in the adaption of different sets of analytes. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  4. 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.

  5. 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

  6. 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

  7. Adjusting for selection bias in longitudinal analyses using simultaneous equations modeling: the relationship between employment transitions and mental health.

    Science.gov (United States)

    Steele, Fiona; French, Robert; Bartley, Mel

    2013-09-01

    Effects of labor force participation on mental health can be difficult to discern due to the possibility of selection bias. Previous research typically adjusts for direct selection (reverse causality) but ignores indirect selection (unmeasured confounders). We investigate the relationship between men's employment transitions and mental health using a dynamic simultaneous equations model applied to data from the British Household Panel Survey (1991-2009). Outcome is self-reported distress and anxiety as summed on a 12-point scale. We allow for direct selection by allowing prior mental health to affect both subsequent mental health and employment transitions in the joint model. We adjust for indirect selection by allowing for residual correlation between mental health and employment. Moving from unemployment to employment was strongly associated with an improvement in mental health, whereas becoming unemployed was detrimental. However, these associations were attenuated by unmeasured confounders. After adjustment for indirect selection, the increased distress and anxiety associated with becoming unemployed decreased from 2.5 (95% confidence interval = 2.2 to 2.7) to 2.2 (2.0 to 2.5). (A change of 2.5 equates to half a standard deviation on the 12-point scale.) The improvement with moving from unemployment to employment was also weakened slightly (from -2.1 [-2.4 to -1.7] to -1.8 [-2.1 to -1.5]). There was strong evidence of indirect selection, but less support for direct selection. Nevertheless, the effects on psychological health of transitions between employment and unemployment, and between employment and economic inactivity, remained substantial after adjusting for selection.

  8. 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

  9. 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...

  10. Dynamic Modeling of Adjustable-Speed Pumped Storage Hydropower Plant: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Muljadi, E.; Singh, M.; Gevorgian, V.; Mohanpurkar, M.; Havsapian, R.; Koritarov, V.

    2015-04-06

    Hydropower is the largest producer of renewable energy in the U.S. More than 60% of the total renewable generation comes from hydropower. There is also approximately 22 GW of pumped storage hydropower (PSH). Conventional PSH uses a synchronous generator, and thus the rotational speed is constant at synchronous speed. This work details a hydrodynamic model and generator/power converter dynamic model. The optimization of the hydrodynamic model is executed by the hydro-turbine controller, and the electrical output real/reactive power is controlled by the power converter. All essential controllers to perform grid-interface functions and provide ancillary services are included in the model.

  11. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

    Directory of Open Access Journals (Sweden)

    D. Verfaillie

    2017-11-01

    Full Text Available We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods, the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally

  12. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

    Science.gov (United States)

    Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

    2017-11-01

    We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for

  13. Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model.

    Science.gov (United States)

    Dropkin, Greg

    2016-11-24

    Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

  14. Real time adjustment of slow changing flow components in distributed urban runoff models

    DEFF Research Database (Denmark)

    Borup, Morten; Grum, M.; Mikkelsen, Peter Steen

    2011-01-01

    In many urban runoff systems infiltrating water contributes with a substantial part of the total inflow and therefore most urban runoff modelling packages include hydrological models for simulating the infiltrating inflow. This paper presents a method for deterministic updating of the hydrological...

  15. Bias-Adjusted Three-Step Latent Markov Modeling With Covariates

    NARCIS (Netherlands)

    Di Mari, Roberto; Oberski, Daniel L.; Vermunt, Jeroen K.

    2016-01-01

    Latent Markov models with covariates can be estimated via 1-step maximum likelihood. However, this 1-step approach has various disadvantages, such as that the inclusion of covariates in the model might alter the formation of the latent states and that parameter estimation could become infeasible

  16. Chiropractic Adjustment

    Science.gov (United States)

    ... Results Chiropractic adjustment can be effective in treating low back pain, although much of the research done shows only a modest benefit — similar to the results of more conventional treatments. Some studies suggest that spinal manipulation also may ...

  17. A Dirichlet process mixture model for survival outcome data: assessing nationwide kidney transplant centers.

    Science.gov (United States)

    Zhao, Lili; Shi, Jingchunzi; Shearon, Tempie H; Li, Yi

    2015-04-15

    Mortality rates are probably the most important indicator for the performance of kidney transplant centers. Motivated by the national evaluation of mortality rates at kidney transplant centers in the USA, we seek to categorize the transplant centers based on the mortality outcome. We describe a Dirichlet process model and a Dirichlet process mixture model with a half-cauchy prior for the estimation of the risk-adjusted effects of the transplant centers, with strategies for improving the model performance, interpretability, and classification ability. We derive statistical measures and create graphical tools to rate transplant centers and identify outlying groups of centers with exceptionally good or poor performance. The proposed method was evaluated through simulation and then applied to assess kidney transplant centers from a national organ failure registry. Copyright © 2015 John Wiley & Sons, Ltd.

  18. Linear identification and model adjustment of a PEM fuel cell stack

    Energy Technology Data Exchange (ETDEWEB)

    Kunusch, C.; Puleston, P.F.; More, J.J. [LEICI, Departamento de Electrotecnia, Universidad Nacional de La Plata, calle 1 esq. 47 s/n, 1900 La Plata (Argentina); Consejo de Investigaciones Cientificas y Tecnicas (CONICET) (Argentina); Husar, A. [Institut de Robotica i Informatica Industrial (CSIC-UPC), c/ Llorens i Artigas 4-6, 08028 Barcelona (Spain); Mayosky, M.A. [LEICI, Departamento de Electrotecnia, Universidad Nacional de La Plata, calle 1 esq. 47 s/n, 1900 La Plata (Argentina); Comision de Investigaciones Cientificas (CIC), Provincia de Buenos Aires (Argentina)

    2008-07-15

    In the context of fuel cell stack control a mayor challenge is modeling the interdependence of various complex subsystem dynamics. In many cases, the states interaction is usually modeled through several look-up tables, decision blocks and piecewise continuous functions. Many internal variables are inaccessible for measurement and cannot be used in control algorithms. To make significant contributions in this area, it is necessary to develop reliable models for control and design purposes. In this paper, a linear model based on experimental identification of a 7-cell stack was developed. The procedure followed to obtain a linear model of the system consisted in performing spectroscopy tests of four different single-input single-output subsystems. The considered inputs for the tests were the stack current and the cathode oxygen flow rate, while the measured outputs were the stack voltage and the cathode total pressure. The resulting model can be used either for model-based control design or for on-line analysis and errors detection. (author)

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. The combined geodetic network adjusted on the reference ellipsoid – a comparison of three functional models for GNSS observations

    Directory of Open Access Journals (Sweden)

    Kadaj Roman

    2016-12-01

    Full Text Available The adjustment problem of the so-called combined (hybrid, integrated network created with GNSS vectors and terrestrial observations has been the subject of many theoretical and applied works. The network adjustment in various mathematical spaces was considered: in the Cartesian geocentric system on a reference ellipsoid and on a mapping plane. For practical reasons, it often takes a geodetic coordinate system associated with the reference ellipsoid. In this case, the Cartesian GNSS vectors are converted, for example, into geodesic parameters (azimuth and length on the ellipsoid, but the simple form of converted pseudo-observations are the direct differences of the geodetic coordinates. Unfortunately, such an approach may be essentially distorted by a systematic error resulting from the position error of the GNSS vector, before its projection on the ellipsoid surface. In this paper, an analysis of the impact of this error on the determined measures of geometric ellipsoid elements, including the differences of geodetic coordinates or geodesic parameters is presented. Assuming that the adjustment of a combined network on the ellipsoid shows that the optimal functional approach in relation to the satellite observation, is to create the observational equations directly for the original GNSS Cartesian vector components, writing them directly as a function of the geodetic coordinates (in numerical applications, we use the linearized forms of observational equations with explicitly specified coefficients. While retaining the original character of the Cartesian vector, one avoids any systematic errors that may occur in the conversion of the original GNSS vectors to ellipsoid elements, for example the vector of the geodesic parameters. The problem is theoretically developed and numerically tested. An example of the adjustment of a subnet loaded from the database of reference stations of the ASG-EUPOS system was considered for the preferred functional

  4. Elasto-plastic hardening models adjustment to ferritic, austenitic and austenoferritic Rebar

    Directory of Open Access Journals (Sweden)

    Beatriz Hortigón

    2017-05-01

    Full Text Available The elastoplastic behaviour of steel used for structural member fabrication has received attention to facilitate a mechanical-resistant design. New Zealand and South African standards have adopted various theoretical approaches to describe such behaviour in stainless steels. With respect to the building industry, describing the tensile behaviour of steel rebar used to produce reinforced concrete structures is of interest. Differences compared with the homogenous material described in the above mentioned standards and related literatures are discussed in this paper. Specifically, the presence of ribs and the TEMPCORE® technology used to produce carbon steel rebar may alter the elastoplastic model. Carbon steel rebar is shown to fit a Hollomon model giving hardening exponent values on the order of 0.17. Austenitic stainless steel rebar behaviour is better described using a modified Rasmussen model with a free fitted exponent of 6. Duplex stainless steel shows a poor fit to any previous model.

  5. Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results.

    Science.gov (United States)

    Mantopoulos, Theodoros; Mitchell, Paul M; Welton, Nicky J; McManus, Richard; Andronis, Lazaros

    2016-11-01

    Statistical models employed in analysing patient-level cost and effectiveness data need to be flexible enough to adjust for any imbalanced covariates, account for correlations between key parameters, and accommodate potential skewed distributions of costs and/or effects. We compare prominent statistical models for cost-effectiveness analysis alongside randomised controlled trials (RCTs) and covariate adjustment to assess their performance and accuracy using data from a large RCT. Seemingly unrelated regressions, linear regression of net monetary benefits, and Bayesian generalized linear models with various distributional assumptions were used to analyse data from the TASMINH2 trial. Each model adjusted for covariates prognostic of costs and outcomes. Cost-effectiveness results were notably sensitive to model choice. Models assuming normally distributed costs and effects provided a poor fit to the data, and potentially misleading inference. Allowing for a beta distribution captured the true incremental difference in effects and changed the decision as to which treatment is preferable. Our findings suggest that Bayesian generalized linear models which allow for non-normality in estimation offer an attractive tool for researchers undertaking cost-effectiveness analyses. The flexibility provided by such methods allows the researcher to analyse patient-level data which are not necessarily normally distributed, while at the same time it enables assessing the effect of various baseline covariates on cost-effectiveness results.

  6. Modeling and simulation of M/M/c queuing pharmacy system with adjustable parameters

    Science.gov (United States)

    Rashida, A. R.; Fadzli, Mohammad; Ibrahim, Safwati; Goh, Siti Rohana

    2016-02-01

    This paper studies a discrete event simulation (DES) as a computer based modelling that imitates a real system of pharmacy unit. M/M/c queuing theo is used to model and analyse the characteristic of queuing system at the pharmacy unit of Hospital Tuanku Fauziah, Kangar in Perlis, Malaysia. The input of this model is based on statistical data collected for 20 working days in June 2014. Currently, patient waiting time of pharmacy unit is more than 15 minutes. The actual operation of the pharmacy unit is a mixed queuing server with M/M/2 queuing model where the pharmacist is referred as the server parameters. DES approach and ProModel simulation software is used to simulate the queuing model and to propose the improvement for queuing system at this pharmacy system. Waiting time for each server is analysed and found out that Counter 3 and 4 has the highest waiting time which is 16.98 and 16.73 minutes. Three scenarios; M/M/3, M/M/4 and M/M/5 are simulated and waiting time for actual queuing model and experimental queuing model are compared. The simulation results show that by adding the server (pharmacist), it will reduce patient waiting time to a reasonable improvement. Almost 50% average patient waiting time is reduced when one pharmacist is added to the counter. However, it is not necessary to fully utilize all counters because eventhough M/M/4 and M/M/5 produced more reduction in patient waiting time, but it is ineffective since Counter 5 is rarely used.

  7. Model-based Adjustment of Droplet Characteristic for 3D Electronic Printing

    Directory of Open Access Journals (Sweden)

    Lin Na

    2017-01-01

    Full Text Available The major challenge in 3D electronic printing is the print resolution and accuracy. In this paper, a typical mode - lumped element modeling method (LEM - is adopted to simulate the droplet jetting characteristic. This modeling method can quickly get the droplet velocity and volume with a high accuracy. Experimental results show that LEM has a simpler structure with the sufficient simulation and prediction accuracy.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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/.

  11. 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.

  12. 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.

  13. A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

    Science.gov (United States)

    Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

    2017-04-01

    Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the

  14. Adjusting for mortality effects in chronic toxicity testing: Mixture model approach

    Energy Technology Data Exchange (ETDEWEB)

    Wang, S.C.D.; Smith, E.P.

    2000-01-01

    Chronic toxicity tests, such as the Ceriodaphnia dubia 7-d test are typically analyzed using standard statistical methods such as analysis of variance or regression. Recent research has emphasized the use of Poisson regression or more generalized regression for the analysis of the fecundity data from these studies. A possible problem in using standard statistical techniques is that mortality may occur from toxicant effects as well as reduced fecundity. A mixture model that accounts for fecundity and mortality is proposed for the analysis of data arising from these studies. Inferences about key parameters in the model are discussed. A joint estimate of the inhibition concentration is proposed based on the model. Confidence interval estimations via the bootstrap method is discussed. An example is given for a study involving copper and mercury.

  15. Towards individualized dose constraints: Adjusting the QUANTEC radiation pneumonitis model for clinical risk factors

    DEFF Research Database (Denmark)

    Appelt, Ane L; Vogelius, Ivan R.; Farr, Katherina P.

    2014-01-01

    -response relationships for clinical risk factors was employed. Effect size estimates (odds ratios) for risk factors were drawn from a recently published meta-analysis. Baseline values for D50 and γ50 were found. The method was tested in an independent dataset (103 patients), comparing the predictive power of the dose......-only QUANTEC model and the model including risk factors. Subdistribution cumulative incidence functions were compared for patients with high/low-risk predictions from the two models, and concordance indices (c-indices) for the prediction of RP were calculated. Results. The reference dose- response relationship......Background. Understanding the dose-response of the lung in order to minimize the risk of radiation pneumonitis (RP) is critical for optimization of lung cancer radiotherapy. We propose a method to combine the dose-response relationship for RP in the landmark QUANTEC paper with known clinical risk...

  16. Adjustment and Characterization of an Original Model of Chronic Ischemic Heart Failure in Pig

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    Laurent Barandon

    2010-01-01

    Full Text Available We present and characterize an original experimental model to create a chronic ischemic heart failure in pig. Two ameroid constrictors were placed around the LAD and the circumflex artery. Two months after surgery, pigs presented a poor LV function associated with a severe mitral valve insufficiency. Echocardiography analysis showed substantial anomalies in radial and circumferential deformations, both on the anterior and lateral surface of the heart. These anomalies in function were coupled with anomalies of perfusion observed in echocardiography after injection of contrast medium. No demonstration of myocardial infarction was observed with histological analysis. Our findings suggest that we were able to create and to stabilize a chronic ischemic heart failure model in the pig. This model represents a useful tool for the development of new medical or surgical treatment in this field.

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

    Directory of Open Access Journals (Sweden)

    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

  18. 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

  19. 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.

  20. 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.

  1. 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

  2. The linear quadratic adjustment cost model and the demand for labour

    DEFF Research Database (Denmark)

    Engsted, Tom; Haldrup, Niels

    1994-01-01

    Der udvikles en ny metode til estimation og test af den lineære kvadratiske tilpasningsomkostningsmodel når de underliggende tidsserier er ikke-stationære, og metoden anvendes til modellering af arbejdskraftefterspørgslen i danske industrisektorer....

  3. An improved canopy wind model for predicting wind adjustment factors and wildland fire behavior

    Science.gov (United States)

    W. J. Massman; J. M. Forthofer; M. A. Finney

    2017-01-01

    The ability to rapidly estimate wind speed beneath a forest canopy or near the ground surface in any vegetation is critical to practical wildland fire behavior models. The common metric of this wind speed is the "mid-flame" wind speed, UMF. However, the existing approach for estimating UMF has some significant shortcomings. These include the assumptions that...

  4. Citizens' Perceptions of Flood Hazard Adjustments: An Application of the Protective Action Decision Model

    Science.gov (United States)

    Terpstra, Teun; Lindell, Michael K.

    2013-01-01

    Although research indicates that adoption of flood preparations among Europeans is low, only a few studies have attempted to explain citizens' preparedness behavior. This article applies the Protective Action Decision Model (PADM) to explain flood preparedness intentions in the Netherlands. Survey data ("N" = 1,115) showed that…

  5. Effect of Treatment Education Based on the Roy Adaptation Model on Adjustment of Hemodialysis Patients.

    Science.gov (United States)

    Kacaroglu Vicdan, Ayse; Gulseven Karabacak, Bilgi

    2016-01-01

    The Roy Adaptation Model examines the individual in 4 fields: physiological mode, self-concept mode, role function mode, and interdependence mode. Hemodialysis treatment is associated with the Roy Adaptation Model as it involves fields that might be needed by the individual with chronic renal disease. This research was conducted as randomized controlled experiment with the aim of determining the effect of the education given in accordance with the Roy Adaptation Model on physiological, psychological, and social adaptation of individuals undergoing hemodialysis treatment. This was a random controlled experimental study. The study was conducted at a dialysis center in Konya-Aksehir in Turkey between July 1 and December 31, 2012. The sample was composed of 82 individuals-41 experimental and 41 control. In the second interview, there was a decrease in the systolic blood pressures and body weights of the experimental group, an increase in the scores of functional performance and self-respect, and a decrease in the scores of psychosocial adaptation. In the control group, on the other hand, there was a decrease in the scores of self-respect and an increase in the scores of psychosocial adaptation. The 2 groups were compared in terms of adaptation variables and a difference was determined on behalf of the experimental group. The training that was provided and evaluated for individuals receiving hemodialysis according to 4 modes of the Roy Adaptation Model increased physical, psychological, and social adaptation.

  6. Preserving Heterogeneity and Consistency in Hydrological Model Inversions by Adjusting Pedotransfer Functions

    Science.gov (United States)

    Numerical modeling is the dominant method for quantifying water flow and the transport of dissolved constituents in surface soils as well as the deeper vadose zone. While the fundamental laws that govern the mechanics of the flow processes in terms of Richards' and convection-dispersion equations a...

  7. Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts

    Science.gov (United States)

    Mastin, Larry G.; Van Eaton, Alexa; Durant, A.J.

    2016-01-01

    Volcanic ash transport and dispersion (VATD) models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters), thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16–17 September 1992 Crater Peak (Mount Spurr); 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m−3. We evaluated the fit using three indices that compare modeled versus measured (1) mass load at sample locations; (2) mass load versus distance along the dispersal axis; and (3) isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between  ∼  2.3 and 2.7φ (0.20–0.15 mm), despite large variations in erupted mass (0.25–50 Tg), plume height (8.5–25 km), mass fraction of fine ( <  0.063 mm) ash (3–59 %), atmospheric temperature, and water content between these eruptions. This close agreement suggests that aggregation may be treated as a discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.

  8. Adjusting particle-size distributions to account for aggregation in tephra-deposit model forecasts

    Directory of Open Access Journals (Sweden)

    L. G. Mastin

    2016-07-01

    Full Text Available Volcanic ash transport and dispersion (VATD models are used to forecast tephra deposition during volcanic eruptions. Model accuracy is limited by the fact that fine-ash aggregates (clumps into clusters, thus altering patterns of deposition. In most models this is accounted for by ad hoc changes to model input, representing fine ash as aggregates with density ρagg, and a log-normal size distribution with median μagg and standard deviation σagg. Optimal values may vary between eruptions. To test the variance, we used the Ash3d tephra model to simulate four deposits: 18 May 1980 Mount St. Helens; 16–17 September 1992 Crater Peak (Mount Spurr; 17 June 1996 Ruapehu; and 23 March 2009 Mount Redoubt. In 192 simulations, we systematically varied μagg and σagg, holding ρagg constant at 600 kg m−3. We evaluated the fit using three indices that compare modeled versus measured (1 mass load at sample locations; (2 mass load versus distance along the dispersal axis; and (3 isomass area. For all deposits, under these inputs, the best-fit value of μagg ranged narrowly between  ∼  2.3 and 2.7φ (0.20–0.15 mm, despite large variations in erupted mass (0.25–50 Tg, plume height (8.5–25 km, mass fraction of fine ( <  0.063 mm ash (3–59 %, atmospheric temperature, and water content between these eruptions. This close agreement suggests that aggregation may be treated as a discrete process that is insensitive to eruptive style or magnitude. This result offers the potential for a simple, computationally efficient parameterization scheme for use in operational model forecasts. Further research may indicate whether this narrow range also reflects physical constraints on processes in the evolving cloud.

  9. SURVIVAL OF MICROORGANISMS FROM MODERN PROBIOTICS IN MODEL CONDITIONS OF THE INTESTINE

    Directory of Open Access Journals (Sweden)

    Kabluchko TV

    2017-03-01

    Full Text Available Introduction. The staye of intestinal microflora affects the work of the whole organism. When composition of normal ibtestine microflora changes, its restoration is required. In our days a wide variety of probiotic drugs are available on the market which can be used to solve this problem. Most bacteria having probiotic properties represent the families Lactobacillus and Bifidobacterium, which have poor resistance to acidic content of the stomach and toxic effects of bile salts. Various studies have clearly shown that in a person with normal acidic and bile secretion, the lactobacilli and bifidobacteria are not detected after the passage through the duodenum, i.e., they perish before reaching the small intestines. In this study we compared the survival of different microorganisms which are contained in 9 probiotic drugs in a model of gastric and intestinal environments. Material and methods. In the laboratory of SI: “Mechnikov Institute Microbiology and Immunology, National Ukrainian Academy Medical Sciences" the in vitro experiments have been evaluated to test the ability of different probiotic bacteria which were contained in 9 probiotic drugs to survive the impact of the model environment of the stomach and duodenum. Bacillus coagulans persistence was evaluated under impact of simulated environment of the stomach and duodenum, it also was assessed by the quantity of CFU by incubation on culture medium. The following were studied: Lactobacillus acidophilus, Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus casei, Lactobacillus plantarum, Lactobacillus bulgaricus, Bifidobacterium bifidum, Bifidobacterium longum , Bifidobacterium breve, Bifidobacterium infantis, Bifidobacterium animalis subsp. Lactis BB-12, Saccharomyces boulardii, Bacillus coagulans, Bacillus clausii, Enterococcus faecium. Microorganisms were incubated for 3 hours in a model environment of the stomach (pepsin 3 g / l, hydrochloric acid of 160 mmol / l, pH 2

  10. The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers.

    Science.gov (United States)

    Kimura, Akio; Nakahara, Shinji; Chadbunchachai, Witaya

    2012-02-02

    For real-time assessment of the probability of survival (Ps) of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients. The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS) method were extracted from the Japan Trauma Data Bank (2004-2007) and analyzed. Half (5,113) of the data was allocated to a derivation data set, with the other half (5,097) allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS), the Glasgow Coma Scale score (GCS), systolic blood pressure (SBP), respiratory rate (RR), and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4) as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC) was calculated for each model to measure discriminant ability. A group of formulas (log (Ps/1-Ps) = logit (Ps) = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing) was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents. © 2012 Kimura et al; licensee BioMed Central Ltd.

  11. The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers

    Directory of Open Access Journals (Sweden)

    Kimura Akio

    2012-02-01

    Full Text Available Abstract Background For real-time assessment of the probability of survival (Ps of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients. Methods The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS method were extracted from the Japan Trauma Data Bank (2004-2007 and analyzed. Half (5,113 of the data was allocated to a derivation data set, with the other half (5,097 allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS, the Glasgow Coma Scale score (GCS, systolic blood pressure (SBP, respiratory rate (RR, and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4 as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC was calculated for each model to measure discriminant ability. Results A group of formulas (log (Ps/1-Ps = logit (Ps = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. Conclusion: These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents.

  12. Modelling and control of Base Plate Loading subsystem for The Motorized Adjustable Vertical Platform

    Science.gov (United States)

    Norsahperi, N. M. H.; Ahmad, S.; Fuad, A. F. M.; Mahmood, I. A.; Toha, S. F.; Akmeliawati, R.; Darsivan, F. J.

    2017-03-01

    Malaysia National Space Agency, ANGKASA is an organization that intensively undergoes many researches especially on space. On 2011, ANGKASA had built Satellite Assembly, Integration and Test Centre (AITC) for spacecraft development and test. Satellite will undergo numerous tests and one of it is Thermal test in Thermal Vacuum Chamber (TVC). In fact, TVC is located in cleanroom and on a platform. The only available facilities for loading and unloading the satellite is overhead crane. By utilizing the overhead crane can jeopardize the safety of the satellite. Therefore, Motorized vertical platform (MAVeP) for transferring the satellite into the TVC with capability to operate under cleanroom condition and limited space is proposed to facilitate the test. MAVeP is the combination of several mechanisms to produce horizontal and vertical motions with the ability to transfer the satellite from loading bay into TVC. The integration of both motions to elevate and transfer heavy loads with high precision capability will deliver major contributions in various industries such as aerospace and automotive. Base plate subsystem is capable to translate the horizontal motion by converting the angular motion from motor to linear motion by using rack and pinion mechanism. Generally a system can be modelled by performing physical modelling from schematic diagram or through system identification techniques. Both techniques are time consuming and required comprehensive understanding about the system, which may expose to error prone especially for complex mechanism. Therefore, a 3D virtual modelling technique has been implemented to represent the system in real world environment i.e. gravity to simulate control performance. The main purpose of this technique is to provide better model to analyse the system performance and capable to evaluate the dynamic behaviour of the system with visualization of the system performance, where a 3D prototype was designed and assembled in Solidworks

  13. Risk-adjusted econometric model to estimate postoperative costs: an additional instrument for monitoring performance after major lung resection.

    Science.gov (United States)

    Brunelli, Alessandro; Salati, Michele; Refai, Majed; Xiumé, Francesco; Rocco, Gaetano; Sabbatini, Armando

    2007-09-01

    The objectives of this study were to develop a risk-adjusted model to estimate individual postoperative costs after major lung resection and to use it for internal economic audit. Variable and fixed hospital costs were collected for 679 consecutive patients who underwent major lung resection from January 2000 through October 2006 at our unit. Several preoperative variables were used to develop a risk-adjusted econometric model from all patients operated on during the period 2000 through 2003 by a stepwise multiple regression analysis (validated by bootstrap). The model was then used to estimate the postoperative costs in the patients operated on during the 3 subsequent periods (years 2004, 2005, and 2006). Observed and predicted costs were then compared within each period by the Wilcoxon signed rank test. Multiple regression and bootstrap analysis yielded the following model predicting postoperative cost: 11,078 + 1340.3X (age > 70 years) + 1927.8X cardiac comorbidity - 95X ppoFEV1%. No differences between predicted and observed costs were noted in the first 2 periods analyzed (year 2004, $6188.40 vs $6241.40, P = .3; year 2005, $6308.60 vs $6483.60, P = .4), whereas in the most recent period (2006) observed costs were significantly lower than the predicted ones ($3457.30 vs $6162.70, P < .0001). Greater precision in predicting outcome and costs after therapy may assist clinicians in the optimization of clinical pathways and allocation of resources. Our economic model may be used as a methodologic template for economic audit in our specialty and complement more traditional outcome measures in the assessment of performance.

  14. Models of quality-adjusted life years when health varies over time

    DEFF Research Database (Denmark)

    Hansen, Kristian Schultz; Østerdal, Lars Peter Raahave

    2006-01-01

    time tradeoff (TTO) and standard gamble (SG) scores. We investigate deterministic and probabilistic models and consider five different families of discounting functions in all. The second part of the paper discusses four issues recurrently debated in the literature. This discussion includes questioning...... the SG method as the gold standard for estimation of the health state index, reexamining the role of the constantproportional tradeoff condition, revisiting the problem of double discounting of QALYs, and suggesting that it is not a matter of choosing between TTO and SG procedures as the combination...... of these two can be used to disentangle risk aversion from discounting. We find that caution must be taken when drawing conclusions from models with chronic health states to situations where health varies over time. One notable difference is that in the former case, risk aversion may be indistinguishable from...

  15. Models of Quality-Adjusted Life Years when Health varies over Time: Survey and Analysis

    DEFF Research Database (Denmark)

    Hansen, Kristian Schultz; Østerdal, Lars Peter

    2006-01-01

    time trade-off (TTO) and standard gamble (SG) scores. We investigate deterministic and probabilistic models and consider five different families of discounting functions in all. This discussion includes questioning the SG method as the gold standard of the health state index, re-examining the role...... of the constant-proportional trade-off condition, revisiting the problem of double discounting of QALYs, and suggesting that it is not a matter of choosing between TTO and SG procedures as the combination of these two can be used to disentangle risk aversion from discounting. We find that caution must be taken...... when drawing conclusions from models with chronic health states to situations where health varies over time. One notable difference is that in the former case, risk aversion may be indistinguishable from discounting....

  16. A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

    Science.gov (United States)

    Hapugoda, J. C.; Sooriyarachchi, M. R.

    2017-09-01

    Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

  17. 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.

  18. Analyzing the Survival of Colorectal Cancer Patients of Tehran Taleghani Hospital using Non-Mixture Cure Model

    Directory of Open Access Journals (Sweden)

    Zahra Abdolalian

    2016-12-01

    Full Text Available Abstract Background: 4cure models are a model to analyze survival data that these models exist for long term survivors. Cure models are a special type of survival model where it is assumed that there are a proportion of subjects who had never event, thus, survival curve will eventually reach a plateau. Therefore, standard survival models are not appropriate because they do not account for the possibility of cure.The aim of the present research is to apply non-mixture cure model to analyze survival of patients with colorectal cancer. Materials and Methods: We studied 232 patients with colorectal cancer who were visited and treated at Taleghani Hospital Research Center for Gastroenterology and Liver Disease in Tehran. These patients were diagnosed from 1987 to 2012 and followed up until 2013. The Effect of age, gender, family history, body mass index and site of infection were studied. Kaplan-Meier and Non-Mixture cure Model were used for analzing data. Results: The ten-year survival rate after diagnosis in the studied patients was 64 % .A total of 60 (25.8 % deaths due to colorectal cancer were observed. The mean of age at the time of diagnosis was 51.6 years. Based on non-mixed cure model, the rangs of age was 45-65 years old and BMI were significant. Conclusion: When the population is divided into two groups (susceptible and non- susceptible individuals, using Cox semi-parametric model is not appropriate. Therefore, we should use cure models.

  19. 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.

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

    Science.gov (United States)

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

    2014-01-01

    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 (pANP deficiency (pANP protected against the development of heart failure as indicated by reduced lung water, alveolar congestion, pleural effusions etc. ANP improved systolic function and reduced cardiomegaly. Pathologic cardiac remodeling was diminished in mice with normal ANP as indicated by decreased ventricular interstitial and perivascular fibrosis. Mice with dilated cardiomyopathy and normal ANP levels had better systolic function (pANP-deficiency. Dilated cardiomyopathy was associated with diminished cardiac transcripts for natriuretic peptide receptors A and B in mice with normal ANP and ANP-deficiency but transcripts for natriuretic peptide receptor C and CNP were selectively altered in mice with dilated cardiomyopathy and ANP-deficiency. Taken together, these data indicate that ANP has potent effects in experimental dilated cardiomyopathy that reduce the development of heart failure, prevent pathologic remodeling, preserve systolic function and reduce mortality. Despite the apparent overlap in physiologic function between the natriuretic peptides, these data suggest that the role of ANP in dilated cardiomyopathy and heart failure is not compensated physiologically by other natriuretic peptides. PMID:24379183

  1. Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors.

    Directory of Open Access Journals (Sweden)

    Winyoo Chowanadisai

    Full Text Available The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05 (S2 Table. Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.

  2. Transcolonic Perirectal NOTES Access (PNA): A feasibility study with survival in swine model.

    Science.gov (United States)

    Oliveira, André L A; Zorron, Ricardo; Oliveira, Flavio M M DE; Santos, Marcelo B Dos; Scheffer, Jussara P; Rios, Marcelo; Antunes, Fernanda

    2017-05-01

    Transrectal access still has some unsolved issues such as spatial orientation, infection, access and site closure. This study presents a simple technique to perform transcolonic access with survival in a swine model series. A new technique for NOTES perirectal access to perform retroperitoneoscopy, peritoneoscopy, liver and lymphnode biopsies was performed in 6 pigs, using Totally NOTES technique. The specimens were extracted transanally. The flexible endoscope was inserted through a posterior transmural incision and the retrorectal space. Cultures of bacteria were documented for the retroperitoneal space and intra abdominal cavity after 14 days. Rectal site was closed using non-absorbable sutures. There was no bowel cleansing, nor preoperative fasting. The procedures were performed in 6 pigs through transcolonic natural orifice access using available endoscopic flexible instruments. All animals survived 14 days without complications, and cultures were negative. Histopathologic examination of the rectal closure site showed adequate healing of suture line and no micro abscesses. The results of feasibility and safety of experimental Transcolonic NOTES potentially brings new frontiers and future wider applications for minimally invasive surgery. The treatment of colorectal, abdominal and retroperitoneal diseases through a flexible Perirectal NOTES Access (PNA) is a promising new approach.

  3. 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.

  4. 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

  5. Dynamic gauge adjustment of high-resolution X-band radar data for convective rain storms: Model-based evaluation against measured combined sewer overflow

    DEFF Research Database (Denmark)

    Borup, Morten; Grum, Morten; Linde, Jens Jørgen

    2016-01-01

    Numerous studies have shown that radar rainfall estimates need to be adjusted against rain gauge measurements in order to be useful for hydrological modelling. In the current study we investigate if adjustment can improve radar rainfall estimates to the point where they can be used for modelling...... overflows from urban drainage systems, and we furthermore investigate the importance of the aggregation period of the adjustment scheme. This is done by continuously adjusting X-band radar data based on the previous 5–30 min of rain data recorded by multiple rain gauges and propagating the rainfall...... estimates through a hydraulic urban drainage model. The model is built entirely from physical data, without any calibration, to avoid bias towards any specific type of rainfall estimate. The performance is assessed by comparing measured and modelled water levels at a weir downstream of a highly impermeable...

  6. A Generic Model for Relative Adjustment Between Optical Sensors Using Rigorous Orbit Mechanics

    Directory of Open Access Journals (Sweden)

    B. Islam

    2008-06-01

    Full Text Available The classical calibration or space resection is the fundamental task in photogrammetry. The lack of sufficient knowledge of interior and exterior orientation parameters lead to unreliable results in the photogrammetric process. One of the earliest in approaches using in photogrammetry was the plumb line calibration method. This method is suitable to recover the radial and decentering lens distortion coefficients, while the remaining interior(focal length and principal point coordinates and exterior orientation parameters have to be determined by a complimentary method. As the lens distortion remains very less it not considered as the interior orientation parameters, in the present rigorous sensor model. There are several other available methods based on the photogrammetric collinearity equations, which consider the determination of exterior orientation parameters, with no mention to the simultaneous determination of inner orientation parameters. Normal space resection methods solve the problem using control points, whose coordinates are known both in image and object reference systems. The non-linearity of the model and the problems, in point location in digital images and identifying the maximum GPS measured control points are the main drawbacks of the classical approaches. This paper addresses mathematical model based on the fundamental assumption of collineariy of three points of two Along-Track Stereo imagery sensors and independent object point. Assuming this condition it is possible to extract the exterior orientation (EO parameters for a long strip and single image together, without and with using the control points. Moreover, after extracting the EO parameters the accuracy for satellite data products are compared in with using single and with no control points.

  7. A multiphase three-dimensional multi-relaxation time (MRT) lattice Boltzmann model with surface tension adjustment

    Science.gov (United States)

    Ammar, Sami; Pernaudat, Guillaume; Trépanier, Jean-Yves

    2017-08-01

    The interdependence of surface tension and density ratio is a weakness of pseudo-potential based lattice Boltzmann models (LB). In this paper, we propose a 3D multi-relaxation time (MRT) model for multiphase flows at large density ratios. The proposed model is capable of adjusting the surface tension independently of the density ratio. We also present the 3D macroscopic equations recovered by the proposed forcing scheme. A high order of isotropy for the interaction force is used to reduce the amplitude of spurious currents. The proposed 3D-MRT model is validated by verifying Laplace's law and by analyzing its thermodynamic consistency and the oscillation period of a deformed droplet. The model is then applied to the simulation of the impact of a droplet on a dry surface. Impact dynamics are determined and the maximum spread factor calculated for different Reynolds and Weber numbers. The numerical results are in agreement with data published in the literature. The influence of surface wettability on the spread factor is also investigated. Finally, our 3D-MRT model is applied to the simulation of the impact of a droplet on a wet surface. The propagation of transverse waves is observed on the liquid surface.

  8. Parenting cognitions → parenting practices → child adjustment? The standard model.

    Science.gov (United States)

    Bornstein, Marc H; Putnick, Diane L; Suwalsky, Joan T D

    2017-06-19

    In a large-scale (N = 317) prospective 8-year longitudinal multiage, multidomain, multivariate, multisource study, we tested a conservative three-term model linking parenting cognitions in toddlerhood to parenting practices in preschool to classroom externalizing behavior in middle childhood, controlling for earlier parenting practices and child externalizing behavior. Mothers who were more knowledgeable, satisfied, and attributed successes in their parenting to themselves when their toddlers were 20 months of age engaged in increased supportive parenting during joint activity tasks 2 years later when their children were 4 years of age, and 6 years after that their 10-year-olds were rated by teachers as having fewer classroom externalizing behavior problems. This developmental cascade of a "standard model" of parenting applied equally to families with girls and boys, and the cascade from parenting attributions to supportive parenting to child externalizing behavior obtained independent of 12 child, parent, and family covariates. Conceptualizing socialization in terms of cascades helps to identify points of effective intervention.

  9. Modeling of Autovariator Operation as Power Components Adjuster in Adaptive Machine Drives

    Science.gov (United States)

    Balakin, P. D.; Belkov, V. N.; Shtripling, L. O.

    2018-01-01

    Full application of the available power and stationary mode preservation for the power station (engine) operation of the transport machine under the conditions of variable external loading, are topical issues. The issues solution is possible by means of mechanical drives with the autovaried rate transfer function and nonholonomic constraint of the main driving mediums. Additional to the main motion, controlled motion of the driving mediums is formed by a variable part of the transformed power flow and is implemented by the integrated control loop, functioning only on the basis of the laws of motion. The mathematical model of the mechanical autovariator operation is developed using Gibbs function, acceleration energy; the study results are presented; on their basis, the design calculations of the autovariator driving mediums and constraints, including its automatic control loop, are possible.

  10. 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...

  11. An ice flow modeling perspective on bedrock adjustment patterns of the Greenland ice sheet

    Directory of Open Access Journals (Sweden)

    M. Olaizola

    2012-11-01

    Full Text Available Since the launch in 2002 of the Gravity Recovery and Climate Experiment (GRACE satellites, several estimates of the mass balance of the Greenland ice sheet (GrIS have been produced. To obtain ice mass changes, the GRACE data need to be corrected for the effect of deformation changes of the Earth's crust. Recently, a new method has been proposed where ice mass changes and bedrock changes are simultaneously solved. Results show bedrock subsidence over almost the entirety of Greenland in combination with ice mass loss which is only half of the currently standing estimates. This subsidence can be an elastic response, but it may however also be a delayed response to past changes. In this study we test whether these subsidence patterns are consistent with ice dynamical modeling results. We use a 3-D ice sheet–bedrock model with a surface mass balance forcing based on a mass balance gradient approach to study the pattern and magnitude of bedrock changes in Greenland. Different mass balance forcings are used. Simulations since the Last Glacial Maximum yield a bedrock delay with respect to the mass balance forcing of nearly 3000 yr and an average uplift at present of 0.3 mm yr−1. The spatial pattern of bedrock changes shows a small central subsidence as well as more intense uplift in the south. These results are not compatible with the gravity based reconstructions showing a subsidence with a maximum in central Greenland, thereby questioning whether the claim of halving of the ice mass change is justified.

  12. Adjustment of mathematical models and quality of soybean grains in the drying with high temperatures

    Directory of Open Access Journals (Sweden)

    Paulo C. Coradi

    2016-04-01

    Full Text Available ABSTRACT The aim of this study was to evaluate the influence of the initial moisture content of soybeans and the drying air temperatures on drying kinetics and grain quality, and find the best mathematical model that fit the experimental data of drying, effective diffusivity and isosteric heat of desorption. The experimental design was completely randomized (CRD, with a factorial scheme (4 x 2, four drying temperatures (75, 90, 105 and 120 ºC and two initial moisture contents (25 and 19% d.b., with three replicates. The initial moisture content of the product interferes with the drying time. The model of Wang and Singh proved to be more suitable to describe the drying of soybeans to temperature ranges of the drying air of 75, 90, 105 and 120 °C and initial moisture contents of 19 and 25% (d.b.. The effective diffusivity obtained from the drying of soybeans was higher (2.5 x 10-11 m2 s-1 for a temperature of 120 °C and water content of 25% (d.b.. Drying of soybeans at higher temperatures (above 105 °C and higher initial water content (25% d.b. also increases the amount of energy (3894.57 kJ kg-1, i.e., the isosteric heat of desorption necessary to perform the process. Drying air temperature and different initial moisture contents affected the quality of soybean along the drying time (electrical conductivity of 540.35 µS cm-1g-1; however, not affect the final yield of the oil extracted from soybean grains (15.69%.

  13. DISCRETE ELEMENT MODELING OF BLADE–STRIKE FREQUENCY AND SURVIVAL OF FISH PASSING THROUGH HYDROKINETIC TURBINES

    Energy Technology Data Exchange (ETDEWEB)

    Romero Gomez, Pedro DJ; Richmond, Marshall C.

    2014-04-17

    Evaluating the consequences from blade-strike of fish on marine hydrokinetic (MHK) turbine blades is essential for incorporating environmental objectives into the integral optimization of machine performance. For instance, experience with conventional hydroelectric turbines has shown that innovative shaping of the blade and other machine components can lead to improved designs that generate more power without increased impacts to fish and other aquatic life. In this work, we used unsteady computational fluid dynamics (CFD) simulations of turbine flow and discrete element modeling (DEM) of particle motion to estimate the frequency and severity of collisions between a horizontal axis MHK tidal energy device and drifting aquatic organisms or debris. Two metrics are determined with the method: the strike frequency and survival rate estimate. To illustrate the procedure step-by-step, an exemplary case of a simple runner model was run and compared against a probabilistic model widely used for strike frequency evaluation. The results for the exemplary case showed a strong correlation between the two approaches. In the application case of the MHK turbine flow, turbulent flow was modeled using detached eddy simulation (DES) in conjunction with a full moving rotor at full scale. The CFD simulated power and thrust were satisfactorily comparable to experimental results conducted in a water tunnel on a reduced scaled (1:8.7) version of the turbine design. A cloud of DEM particles was injected into the domain to simulate fish or debris that were entrained into the turbine flow. The strike frequency was the ratio of the count of colliding particles to the crossing sample size. The fish length and approaching velocity were test conditions in the simulations of the MHK turbine. Comparisons showed that DEM-based frequencies tend to be greater than previous results from Lagrangian particles and probabilistic models, mostly because the DEM scheme accounts for both the geometric

  14. End-Tidal CO2-Guided Chest Compression Delivery Improves Survival in a Neonatal Asphyxial Cardiac Arrest Model.

    Science.gov (United States)

    Hamrick, Justin T; Hamrick, Jennifer L; Bhalala, Utpal; Armstrong, Jillian S; Lee, Jeong-Hoo; Kulikowicz, Ewa; Lee, Jennifer K; Kudchadkar, Sapna R; Koehler, Raymond C; Hunt, Elizabeth A; Shaffner, Donald H

    2017-11-01

    To determine whether end-tidal CO2-guided chest compression delivery improves survival over standard cardiopulmonary resuscitation after prolonged asphyxial arrest. Preclinical randomized controlled study. University animal research laboratory. 1-2-week-old swine. After undergoing a 20-minute asphyxial arrest, animals received either standard or end-tidal CO2-guided cardiopulmonary resuscitation. In the standard group, chest compression delivery was optimized by video and verbal feedback to maintain the rate, depth, and release within published guidelines. In the end-tidal CO2-guided group, chest compression rate and depth were adjusted to obtain a maximal end-tidal CO2 level without other feedback. Cardiopulmonary resuscitation included 10 minutes of basic life support followed by advanced life support for 10 minutes or until return of spontaneous circulation. Mean end-tidal CO2 at 10 minutes of cardiopulmonary resuscitation was 34 ± 8 torr in the end-tidal CO2 group (n = 14) and 19 ± 9 torr in the standard group (n = 14; p = 0.0001). The return of spontaneous circulation rate was 7 of 14 (50%) in the end-tidal CO2 group and 2 of 14 (14%) in the standard group (p = 0.04). The chest compression rate averaged 143 ± 10/min in the end-tidal CO2 group and 102 ± 2/min in the standard group (p CO2-guided chest compression delivery. The response of the relaxation arterial pressure and cerebral perfusion pressure to the initial epinephrine administration was greater in the end-tidal CO2 group than in the standard group (p = 0.01 and p = 0.03, respectively). The prevalence of resuscitation-related injuries was similar between groups. End-tidal CO2-guided chest compression delivery is an effective resuscitation method that improves early survival after prolonged asphyxial arrest in this neonatal piglet model. Optimizing end-tidal CO2 levels during cardiopulmonary resuscitation required that chest compression delivery rate exceed current guidelines. The

  15. 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.

  16. 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.

  17. 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.

  18. Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

    Science.gov (United States)

    Forfait-Dubuc, Carole; Charles, Sandrine; Billoir, Elise; Delignette-Muller, Marie Laure

    2012-05-01

    In ecotoxicology, critical effect concentrations are the most common indicators to quantitatively assess risks for species exposed to contaminants. Three types of critical effect concentrations are classically used: lowest/ no observed effect concentration (LOEC/NOEC), LC( x) (x% lethal concentration) and NEC (no effect concentration). In this article, for each of these three types of critical effect concentration, we compared methods or models used for their estimation and proposed one as the most appropriate. We then compared these critical effect concentrations to each other. For that, we used nine survival data sets corresponding to D. magna exposition to nine different contaminants, for which the time-course of the response was monitored. Our results showed that: (i) LOEC/NOEC values at day 21 were method-dependent, and that the Cochran-Armitage test with a step-down procedure appeared to be the most protective for the environment; (ii) all tested concentration-response models we compared gave close values of LC50 at day 21, nevertheless the Weibull model had the lowest global mean deviance; (iii) a simple threshold NEC-model both concentration and time dependent more completely described whole data (i.e. all timepoints) and enabled a precise estimation of the NEC. We then compared the three critical effect concentrations and argued that the use of the NEC might be a good option for environmental risk assessment.

  19. 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.

  20. Estimation of direct effects for survival data by using the Aalen additive hazards model

    DEFF Research Database (Denmark)

    Martinussen, T.; Vansteelandt, S.; Gerster, M.

    2011-01-01

    We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned...... Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first......-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration....

  1. Fast Cloud Adjustment to Increasing CO2 in a Superparameterized Climate Model

    Directory of Open Access Journals (Sweden)

    Marat Khairoutdinov

    2012-05-01

    Full Text Available Two-year simulation experiments with a superparameterized climate model, SP-CAM, are performed to understand the fast tropical (30S-30N cloud response to an instantaneous quadrupling of CO2 concentration with SST held fixed at present-day values.The greenhouse effect of the CO2 perturbation quickly warms the tropical land surfaces by an average of 0.5 K. This shifts rising motion, surface precipitation, and cloud cover at all levels from the ocean to the land, with only small net tropical-mean cloud changes. There is a widespread average reduction of about 80 m in the depth of the trade inversion capping the marine boundary layer (MBL over the cooler subtropical oceans.One apparent contributing factor is CO2-enhanced downwelling longwave radiation, which reduces boundary-layer radiative cooling, a primary driver of turbulent entrainment through the trade inversion. A second contributor is a slight CO2-induced heating of the free troposphere above the MBL, which strengthens the trade inversion and also inhibits entrainment. There is a corresponding downward displacement of MBL clouds with a very slight decrease in mean cloud cover and albedo.Two-dimensional cloud-resolving model (CRM simulations of this MBL response are run to steady state using composite SP-CAM simulated thermodynamic and wind profiles from a representative cool subtropical ocean regime, for the control and 4xCO2 cases. Simulations with a CRM grid resolution equal to that of SP-CAM are compared with much finer resolution simulations. The coarse-resolution simulations maintain a cloud fraction and albedo comparable to SP-CAM, but the fine-resolution simulations have a much smaller cloud fraction. Nevertheless, both CRM configurations simulate a reduction in inversion height comparable to SP-CAM. The changes in low cloud cover and albedo in the CRM simulations are small, but both simulations predict a slight reduction in low cloud albedo as in SP-CAM.

  2. Modeling longitudinal data and its impact on survival in observational nephrology studies: tools and considerations.

    Science.gov (United States)

    Streja, Elani; Goldstein, Leanne; Soohoo, Melissa; Obi, Yoshitsugu; Kalantar-Zadeh, Kamyar; Rhee, Connie M

    2017-04-01

    Nephrologists and kidney disease researchers are often interested in monitoring how patients' clinical and laboratory measures change over time, what factors may impact these changes, and how these changes may lead to differences in morbidity, mortality, and other outcomes. When longitudinal data with repeated measures over time in the same patients are available, there are a number of analytical approaches that could be employed to describe the trends and changes in these measures, and to explore the associations of these changes with outcomes. Researchers may choose a streamlined and simplified analytic approach to examine trajectories with subsequent outcomes such as estimating deltas (subtraction of the last observation from the first observation) or estimating per patient slopes with linear regression. Conversely, they could more fully address the data complexity by using a longitudinal mixed model to estimate change as a predictor or employ a joint model, which can simultaneously model the longitudinal effect and its impact on an outcome such as survival. In this review, we aim to assist nephrologists and clinical researchers by reviewing these approaches in modeling the association of longitudinal change in a marker with outcomes, while appropriately considering the data complexity. Namely, we will discuss the use of simplified approaches for creating predictor variables representing change in measurements including deltas and patient slopes, as well more sophisticated longitudinal models including joint models, which can be used in addition to simplified models based on the indications and objectives of the study as warranted. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

  3. An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks.

    Science.gov (United States)

    Brown, Grant D; Oleson, Jacob J; Porter, Aaron T

    2016-06-01

    The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach. © 2015, The International Biometric Society.

  4. Low-temperature survival of Salmonella spp. in a model food system with natural microflora.

    Science.gov (United States)

    Morey, Amit; Singh, Manpreet

    2012-03-01

    The United States Department of Agriculture requires chilled poultry carcass temperature to be below 4°C (40°F) to inhibit the growth of Salmonella and improve shelf life. Post-process temperature abuse of chicken leads to proliferation of existing bacteria, including Salmonella, which can lead to the increased risk of human infections. While models predicting Salmonella growth at abusive temperatures are developed using sterile media or chicken slurry, there are limited studies of Salmonella growth in the presence of background microflora at 4-10°C. Experiments in this study were conducted to determine the growth of Salmonella Typhimurium and Heidelberg at 4-10°C in brain heart infusion broth (BHI) and non-sterile chicken slurry (CS). Nalidixic acid-resistant Salmonella Typhimurium and S. Heidelberg (3 log CFU/mL) were inoculated separately in CS and sterile BHI in a 12-well microtiter plate and incubated at 4°C, 7°C, and 10°C, following which samples were taken every 24 h for up to 6 days. Samples from each well (n=5) were spread plated on XLT4 agar+nalidixic acid and incubated at 37°C for 24 h. Bacterial populations were reported as CFU/mL. No significant differences (p>0.05) were observed in the survival of both strains in CS and BHI over the period of 6 days at all temperatures except S. Heidelberg at 7°C. Survival populations of both strains at 4°C were significantly different (p ≤ 0.05) than at 7°C and 10°C in both media types. S. Heidelberg showed a maximum growth of 2 logs in BHI at 10°C among all the treatments. Growth patterns and survival of Salmonella at near refrigeration temperatures during carcass chilling can be useful to develop models to predict Salmonella growth post-processing and during storage, hence assisting processors in improving process controls.

  5. A Price-Dependent Demand Model in the Single Period Inventory System with Price Adjustment

    Directory of Open Access Journals (Sweden)

    Kamran Forghani

    2013-01-01

    Full Text Available The previous efforts toward single period inventory problem with price-dependent demand only investigate the optimal order quantity to minimize the total inventory costs; however, there is no method in the literature to avoid unwanted costs due to the deviation between the actual demand and the previously estimated demand. To fill this gap, the present paper supposes that stochastic demand rate with normal distribution is sensitive to the selling price; this means that increasing the selling price would decrease the demand rate and vice versa. After monitoring the consumption trend within a section of the period, a new selling price is implemented to change the demand rate and reduce the shortage or salvage costs at the end of the period. Three functions were suggested to represent the demand rate as a function of selling price, and the numerical analysis was implemented to solve the proposed problem. Finally, an illustrative numerical example was solved for different configurations in order to show the advantages of the proposed model. The results revealed that there is a significant improvement in the system costs when price revision is considered.

  6. A risk adjusted cost-effectiveness analysis of alternative models of nurse involvement in obesity management in primary care.

    Science.gov (United States)

    Karnon, J; Ali Afzali, H Haji; Gray, J; Holton, C; Banham, D; Beilby, J

    2013-03-01

    Controlled evaluations are subject to uncertainty regarding their replication in the real world, particularly around systems of service provision. Using routinely collected data, we undertook a risk adjusted cost-effectiveness (RAC-E) analysis of alternative applied models of primary health care for the management of obese adult patients. Models were based on the reported level of involvement of practice nurses (registered or enrolled nurses working in general practice) in the provision of clinical-based activities. Linked, routinely collected clinical data describing clinical outcomes (weight, BMI, and obesity-related complications) and resource use (primary care, pharmaceutical, and hospital resource use) were collected. Potential confounders were controlled for using propensity weighted regression analyses. Relative to low level involvement of practice nurses in the provision of clinical-based activities to obese patients, high level involvement was associated with lower costs and better outcomes (more patients losing weight, and larger mean reductions in BMI). Excluding hospital costs, high level practice nurse involvement was associated with slightly higher costs. Incrementally, the high level model gets one additional obese patient to lose weight at an additional cost of $6,741, and reduces mean BMI by an additional one point at an additional cost of $563 (upper 95% confidence interval $1,547). Converted to quality adjusted life year (QALY) gains, the results provide a strong indication that increased involvement of practice nurses in clinical activities is associated with additional health benefits that are achieved at reasonable additional cost. Dissemination activities and incentives are required to encourage general practices to better integrate practice nurses in the active provision of clinical services. Copyright © 2013 The Obesity Society.

  7. Adjustment of Dysregulated Ceramide Metabolism in a Murine Model of Sepsis-Induced Cardiac Dysfunction.

    Science.gov (United States)

    Chung, Ha-Yeun; Kollmey, Anna S; Schrepper, Andrea; Kohl, Matthias; Bläss, Markus F; Stehr, Sebastian N; Lupp, Amelie; Gräler, Markus H; Claus, Ralf A

    2017-04-15

    Cardiac dysfunction, in particular of the left ventricle, is a common and early event in sepsis, and is strongly associated with an increase in patients' mortality. Acid sphingomyelinase (SMPD1)-the principal regulator for rapid and transient generation of the lipid mediator ceramide-is involved in both the regulation of host response in sepsis as well as in the pathogenesis of chronic heart failure. This study determined the degree and the potential role to which SMPD1 and its modulation affect sepsis-induced cardiomyopathy using both genetically deficient and pharmacologically-treated animals in a polymicrobial sepsis model. As surrogate parameters of sepsis-induced cardiomyopathy, cardiac function, markers of oxidative stress as well as troponin I levels were found to be improved in desipramine-treated animals, desipramine being an inhibitor of ceramide formation. Additionally, ceramide formation in cardiac tissue was dysregulated in SMPD1 +/+ as well as SMPD1 -/- animals, whereas desipramine pretreatment resulted in stable, but increased ceramide content during host response. This was a result of elevated de novo synthesis. Strikingly, desipramine treatment led to significantly improved levels of surrogate markers. Furthermore, similar results in desipramine-pretreated SMPD1 -/- littermates suggest an SMPD1-independent pathway. Finally, a pattern of differentially expressed transcripts important for regulation of apoptosis as well as antioxidative and cytokine response supports the concept that desipramine modulates ceramide formation, resulting in beneficial myocardial effects. We describe a novel, protective role of desipramine during sepsis-induced cardiac dysfunction that controls ceramide content. In addition, it may be possible to modulate cardiac function during host response by pre-conditioning with the Food and Drug Administration (FDA)-approved drug desipramine.

  8. Adjustment of Dysregulated Ceramide Metabolism in a Murine Model of Sepsis-Induced Cardiac Dysfunction

    Science.gov (United States)

    Chung, Ha-Yeun; Kollmey, Anna S.; Schrepper, Andrea; Kohl, Matthias; Bläss, Markus F.; Stehr, Sebastian N.; Lupp, Amelie; Gräler, Markus H.; Claus, Ralf A.

    2017-01-01

    Cardiac dysfunction, in particular of the left ventricle, is a common and early event in sepsis, and is strongly associated with an increase in patients’ mortality. Acid sphingomyelinase (SMPD1)—the principal regulator for rapid and transient generation of the lipid mediator ceramide—is involved in both the regulation of host response in sepsis as well as in the pathogenesis of chronic heart failure. This study determined the degree and the potential role to which SMPD1 and its modulation affect sepsis-induced cardiomyopathy using both genetically deficient and pharmacologically-treated animals in a polymicrobial sepsis model. As surrogate parameters of sepsis-induced cardiomyopathy, cardiac function, markers of oxidative stress as well as troponin I levels were found to be improved in desipramine-treated animals, desipramine being an inhibitor of ceramide formation. Additionally, ceramide formation in cardiac tissue was dysregulated in SMPD1+/+ as well as SMPD1−/− animals, whereas desipramine pretreatment resulted in stable, but increased ceramide content during host response. This was a result of elevated de novo synthesis. Strikingly, desipramine treatment led to significantly improved levels of surrogate markers. Furthermore, similar results in desipramine-pretreated SMPD1−/− littermates suggest an SMPD1-independent pathway. Finally, a pattern of differentially expressed transcripts important for regulation of apoptosis as well as antioxidative and cytokine response supports the concept that desipramine modulates ceramide formation, resulting in beneficial myocardial effects. We describe a novel, protective role of desipramine during sepsis-induced cardiac dysfunction that controls ceramide content. In addition, it may be possible to modulate cardiac function during host response by pre-conditioning with the Food and Drug Administration (FDA)-approved drug desipramine. PMID:28420138

  9. On the Accuracy of Glacial Isostatic Adjustment Models for Geodetic Observations to Estimate Arctic Ocean Sea-Level Change

    Directory of Open Access Journals (Sweden)

    Zhenwei Huang

    2013-01-01

    Full Text Available Arctic Ocean sea-level change is an important indicator of climate change. Contemporary geodetic observations, including data from tide gages, satellite altimetry and Gravity Recovery and Climate Experiment (GRACE, are sensitive to the effect of the ongoing glacial isostatic adjustment (GIA process. To fully exploit these geodetic observations to study climate related sea-level change, this GIA effect has to be removed. However, significant uncertainty exists with regard to the GIA model, and using different GIA models could lead to different results. In this study we use an ensemble of 14 contemporary GIA models to investigate their differences when they are applied to the above-mentioned geodetic observations to estimate sea-level change in the Arctic Ocean. We find that over the Arctic Ocean a large range of differences exists in GIA models when they are used to remove GIA effect from tide gage and GRACE observations, but with a relatively smaller range for satellite altimetry observations. In addition, we compare the derived sea-level trend from observations after applying different GIA models in the study regions, sea-level trend estimated from long-term tide gage data shows good agreement with altimetry result over the same data span. However the mass component of sea-level change obtained from GRACE data does not agree well with the result derived from steric-corrected altimeter observation due primarily to the large uncertainty of GIA models, errors in the Arctic Ocean altimetry or steric measurements, inadequate data span, or all of the above. We conclude that GIA correction is critical for studying sea-level change over the Arctic Ocean and further improvement in GIA modelling is needed to reduce the current discrepancies among models.

  10. A risk-adjusted economic evaluation of alternative models of involvement of practice nurses in management of type 2 diabetes.

    Science.gov (United States)

    Haji Ali Afzali, H; Gray, J; Beilby, J; Holton, C; Banham, D; Karnon, J

    2013-07-01

    To determine the cost-effectiveness of alternative models of practice nurse involvement in the management of type 2 diabetes within the primary care setting. Linked routinely collected clinical data and resource use (general practitioner visits, hospital services and pharmaceuticals) were used to undertake a risk-adjusted cost-effectiveness analysis of alternative models of care for the management of diabetes patients. These models were based on the reported level of involvement of practice nurses in the provision of clinical-based activities. Potential confounders were controlled for by using propensity score-weighted regression analyses. The impact of alternative models of care on outcomes and costs was measured and incremental cost-effectiveness estimated. The uncertainty around the estimates of cost-effectiveness was illustrated through bootstrapping. Although the difference in total cost between two models of care was not statistically significant, the high-level model was associated with better outcomes (larger mean reductions in HbA(1c)). The upper 95% confidence intervals showed that the incremental cost per 1% decrease in HbA(1c) is only $454, and per one additional patient to achieve an HbA(1c) value of less than 53 mmol/mol (7.0%) is $323. Further analyses showed little uncertainty surrounding the decision to adopt the high-level model. The results provide a strong indication that the high-level model is a cost-effective way of managing diabetes patients. Our findings highlight the need for effective incentives to encourage general practices to better integrate practice nurses in the provision of clinical services. © 2013 The Authors. Diabetic Medicine © 2013 Diabetes UK.

  11. A frailty model for (interval) censored family survival data, applied to the age at onset of non-physical problems

    NARCIS (Netherlands)

    Jonker, M.A.; Boomsma, D.I.

    2010-01-01

    Family survival data can be used to estimate the degree of genetic and environmental contributions to the age at onset of a disease or of a specific event in life. The data can be modeled with a correlated frailty model in which the frailty variable accounts for the degree of kinship within the

  12. Clinical prediction of 5-year survival in systemic sclerosis: validation of a simple prognostic model in EUSTAR centres

    NARCIS (Netherlands)

    Fransen, J.; Popa-Diaconu, D.A.; Hesselstrand, R.; Carreira, P.; Valentini, G.; Beretta, L.; Airo, P.; Inanc, M.; Ullman, S.; Balbir-Gurman, A.; Sierakowski, S.; Allanore, Y.; Czirjak, L.; Riccieri, V.; Giacomelli, R.; Gabrielli, A.; Riemekasten, G.; Matucci-Cerinic, M.; Farge, D.; Hunzelmann, N.; Hoogen, F.H. Van den; Vonk, M.C.

    2011-01-01

    OBJECTIVE: Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually

  13. Salary adjustments

    CERN Multimedia

    HR Department

    2008-01-01

    In accordance with decisions taken by the Finance Committee and Council in December 2007, salaries are adjusted with effect from 1 January 2008. Scale of basic salaries and scale of stipends paid to fellows (Annex R A 5 and R A 6 respectively): increased by 0.71% with effect from 1 January 2008. As a result of the stability of the Geneva consumer price index, following elements do not increase: a) Family Allowance, Child Allowance and Infant Allowance (Annex R A 3). b) Reimbursement of education fees: maximum amounts of reimbursement (Annex R A 4.01) for the academic year 2007/2008. Related adjustments will be implemented, wherever applicable, to Paid Associates and Students. As in the past, the actual percentage increase of each salary position may vary, due to the application of a constant step value and the rounding effects. Human Resources Department Tel. 73566

  14. Salary adjustments

    CERN Multimedia

    HR Department

    2008-01-01

    In accordance with decisions taken by the Finance Committee and Council in December 2007, salaries are adjusted with effect from 1 January 2008. Scale of basic salaries and scale of stipends paid to fellows (Annex R A 5 and R A 6 respectively): increased by 0.71% with effect from 1 January 2008. As a result of the stability of the Geneva consumer price index, the following elements do not increase: a)\tFamily Allowance, Child Allowance and Infant Allowance (Annex R A 3); b)\tReimbursement of education fees: maximum amounts of reimbursement (Annex R A 4.01) for the academic year 2007/2008. Related adjustments will be applied, wherever applicable, to Paid Associates and Students. As in the past, the actual percentage increase of each salary position may vary, due to the application of a constant step value and rounding effects. Human Resources Department Tel. 73566

  15. Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme.

    Science.gov (United States)

    Wang, Hao; Jiang, Jie; Zhang, Guangjun

    2017-04-21

    The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV) optical navigation sensor using the well capacity adjusting (WCA) scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

  16. Celestial Object Imaging Model and Parameter Optimization for an Optical Navigation Sensor Based on the Well Capacity Adjusting Scheme

    Directory of Open Access Journals (Sweden)

    Hao Wang

    2017-04-01

    Full Text Available The simultaneous extraction of optical navigation measurements from a target celestial body and star images is essential for autonomous optical navigation. Generally, a single optical navigation sensor cannot simultaneously image the target celestial body and stars well-exposed because their irradiance difference is generally large. Multi-sensor integration or complex image processing algorithms are commonly utilized to solve the said problem. This study analyzes and demonstrates the feasibility of simultaneously imaging the target celestial body and stars well-exposed within a single exposure through a single field of view (FOV optical navigation sensor using the well capacity adjusting (WCA scheme. First, the irradiance characteristics of the celestial body are analyzed. Then, the celestial body edge model and star spot imaging model are established when the WCA scheme is applied. Furthermore, the effect of exposure parameters on the accuracy of star centroiding and edge extraction is analyzed using the proposed model. Optimal exposure parameters are also derived by conducting Monte Carlo simulation to obtain the best performance of the navigation sensor. Finally, laboratorial and night sky experiments are performed to validate the correctness of the proposed model and optimal exposure parameters.

  17. Comparison of the average surviving fraction model with the integral biologically effective dose model for an optimal irradiation scheme.

    Science.gov (United States)

    Takagi, Ryo; Komiya, Yuriko; Sutherland, Kenneth L; Shirato, Hiroki; Date, Hiroyuki; Mizuta, Masahiro

    2018-01-04

    In this paper, we compare two radiation effect models: the average surviving fraction (ASF) model and the integral biologically effective dose (IBED) model for deriving the optimal irradiation scheme and show the superiority of ASF. Minimizing the effect on an organ at risk (OAR) is important in radiotherapy. The biologically effective dose (BED) model is widely used to estimate the effect on the tumor or on the OAR, for a fixed value of dose. However, this is not always appropriate because the dose is not a single value but is distributed. The IBED and ASF models are proposed under the assumption that the irradiation is distributed. Although the IBED and ASF models are essentially equivalent for deriving the optimal irradiation scheme in the case of uniform distribution, they are not equivalent in the case of non-uniform distribution. We evaluate the differences between them for two types of cancers: high α/β ratio cancer (e.g. lung) and low α/β ratio cancer (e.g. prostate), and for various distributions i.e. various dose-volume histograms. When we adopt the IBED model, the optimal number of fractions for low α/β ratio cancers is reasonable, but for high α/β ratio cancers or for some DVHs it is extremely large. However, for the ASF model, the results keep within the range used in clinical practice for both low and high α/β ratio cancers and for most DVHs. These results indicate that the ASF model is more robust for constructing the optimal irradiation regimen than the IBED model. © The Author(s) 2018. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

  18. 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

  19. Peptides modeled after the alpha-domain of metallothionein induce neurite outgrowth and promote survival of cerebellar granule neurons

    DEFF Research Database (Denmark)

    Asmussen, Johanne Wirenfeldt; Ambjørn, Malene; Bock, Elisabeth

    2009-01-01

    Metallothionein (MT) is a metal-binding protein capable of preventing oxidative stress and apoptotic cell death in the central nervous system of mammals, and hence is of putative therapeutic value in the treatment of neurodegenerative disorders. Recently, we demonstrated that a peptide modeled...... after the beta-domain of MT, EmtinB, induced neurite outgrowth and increased neuronal survival through binding to receptors of the low-density lipoprotein receptor family (LDLR). The present study identified two MT alpha-domain-derived peptide sequences termed EmtinAn and EmtinAc, each consisting of 14...... amino acids, as potent stimulators of neuronal differentiation and survival of primary neurons. In addition, we show that a peptide derived from the N-terminus of the MT beta-domain, EmtinBn, promotes neuronal survival. The neuritogenic and survival promoting effects of EmtinAc, similar to MT and Emtin...

  20. 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.

  1. A risk-adjusted financial model to estimate the cost of a video-assisted thoracoscopic surgery lobectomy programme.

    Science.gov (United States)

    Brunelli, Alessandro; Tentzeris, Vasileios; Sandri, Alberto; McKenna, Alexandra; Liew, Shan Liung; Milton, Richard; Chaudhuri, Nilanjan; Kefaloyannis, Emmanuel; Papagiannopoulos, Kostas

    2016-05-01

    To develop a clinically risk-adjusted financial model to estimate the cost associated with a video-assisted thoracoscopic surgery (VATS) lobectomy programme. Prospectively collected data of 236 VATS lobectomy patients (August 2012-December 2013) were analysed retrospectively. Fixed and variable intraoperative and postoperative costs were retrieved from the Hospital Accounting Department. Baseline and surgical variables were tested for a possible association with total cost using a multivariable linear regression and bootstrap analyses. Costs were calculated in GBP and expressed in Euros (EUR:GBP exchange rate 1.4). The average total cost of a VATS lobectomy was €11 368 (range €6992-€62 535). Average intraoperative (including surgical and anaesthetic time, overhead, disposable materials) and postoperative costs [including ward stay, high dependency unit (HDU) or intensive care unit (ICU) and variable costs associated with management of complications] were €8226 (range €5656-€13 296) and €3029 (range €529-€51 970), respectively. The following variables remained reliably associated with total costs after linear regression analysis and bootstrap: carbon monoxide lung diffusion capacity (DLCO) 0.05) in 86% of the samples. A hypothetical patient with COPD and DLCO less than 60% would cost €4270 more than a patient without COPD and with higher DLCO values (€14 793 vs €10 523). Risk-adjusting financial data can help estimate the total cost associated with VATS lobectomy based on clinical factors. This model can be used to audit the internal financial performance of a VATS lobectomy programme for budgeting, planning and for appropriate bundled payment reimbursements. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  2. A novel survival model of cardioplegic arrest and cardiopulmonary bypass in rats: a methodology paper

    Directory of Open Access Journals (Sweden)

    Podgoreanu Mihai V

    2008-08-01

    Full Text Available Abstract Background Given the growing population of cardiac surgery patients with impaired preoperative cardiac function and rapidly expanding surgical techniques, continued efforts to improve myocardial protection strategies are warranted. Prior research is mostly limited to either large animal models or ex vivo preparations. We developed a new in vivo survival model that combines administration of antegrade cardioplegia with endoaortic crossclamping during cardiopulmonary bypass (CPB in the rat. Methods Sprague-Dawley rats were cannulated for CPB (n = 10. With ultrasound guidance, a 3.5 mm balloon angioplasty catheter was positioned via the right common carotid artery with its tip proximal to the aortic valve. To initiate cardioplegic arrest, the balloon was inflated and cardioplegia solution injected. After 30 min of cardioplegic arrest, the balloon was deflated, ventilation resumed, and rats were weaned from CPB and recovered. To rule out any evidence of cerebral ischemia due to right carotid artery ligation, animals were neurologically tested on postoperative day 14, and their brains histologically assessed. Results Thirty minutes of cardioplegic arrest was successfully established in all animals. Functional assessment revealed no neurologic deficits, and histology demonstrated no gross neuronal damage. Conclusion This novel small animal CPB model with cardioplegic arrest allows for both the study of myocardial ischemia-reperfusion injury as well as new cardioprotective strategies. Major advantages of this model include its overall feasibility and cost effectiveness. In future experiments long-term echocardiographic outcomes as well as enzymatic, genetic, and histologic characterization of myocardial injury can be assessed. In the field of myocardial protection, rodent models will be an important avenue of research.

  3. 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

  4. 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.

  5. Adjustment of regional climate model output for modeling the climatic mass balance of all glaciers on Svalbard.

    NARCIS (Netherlands)

    Möller, M.; Obleitner, F.; Reijmer, C.H.; Pohjola, V.A.; Glowacki, P.; Kohler, J.

    2016-01-01

    Large-scale modeling of glacier mass balance relies often on the output from regional climate models (RCMs). However, the limited accuracy and spatial resolution of RCM output pose limitations on mass balance simulations at subregional or local scales. Moreover, RCM output is still rarely available

  6. Plants modify biological processes to ensure survival following carbon depletion: a Lolium perenne model.

    Directory of Open Access Journals (Sweden)

    Julia M Lee

    Full Text Available BACKGROUND: Plants, due to their immobility, have evolved mechanisms allowing them to adapt to multiple environmental and management conditions. Short-term undesirable conditions (e.g. moisture deficit, cold temperatures generally reduce photosynthetic carbon supply while increasing soluble carbohydrate accumulation. It is not known, however, what strategies plants may use in the long-term to adapt to situations resulting in net carbon depletion (i.e. reduced photosynthetic carbon supply and carbohydrate accumulation. In addition, many transcriptomic experiments have typically been undertaken under laboratory conditions; therefore, long-term acclimation strategies that plants use in natural environments are not well understood. METHODOLOGY/PRINCIPAL FINDINGS: Perennial ryegrass (Lolium perenne L. was used as a model plant to define whether plants adapt to repetitive carbon depletion and to further elucidate their long-term acclimation mechanisms. Transcriptome changes in both lamina and stubble tissues of field-grown plants with depleted carbon reserves were characterised using reverse transcription-quantitative polymerase chain reaction (RT-qPCR. The RT-qPCR data for select key genes indicated that plants reduced fructan degradation, and increased photosynthesis and fructan synthesis capacities following carbon depletion. This acclimatory response was not sufficient to prevent a reduction (P<0.001 in net biomass accumulation, but ensured that the plant survived. CONCLUSIONS: Adaptations of plants with depleted carbon reserves resulted in reduced post-defoliation carbon mobilization and earlier replenishment of carbon reserves, thereby ensuring survival and continued growth. These findings will help pave the way to improve plant biomass production, for either grazing livestock or biofuel purposes.

  7. Gacyclidine improves the survival and reduces motor deficits in a mouse model of amyotrophic lateral sclerosis

    Directory of Open Access Journals (Sweden)

    Yannick Nicolas Gerber

    2013-12-01

    Full Text Available Amyotrophic lateral sclerosis (ALS is a fatal neurodegenerative disorder typified by a massive loss of motor neurons with few therapeutic options. The exact cause of neuronal degeneration is unknown but it is now admitted that ALS is a multifactorial disease with several mechanisms involved including glutamate excitotoxicity. More specifically, N-methyl-D-aspartate (NMDA-mediated cell death and impairment of the glutamate-transport has been suggested to play a key role in ALS pathophysiology. Thus, evaluating NMDAR antagonists is of high therapeutic interest. Gacyclidine, also named GK11, is a high affinity non-competitive NMDAR antagonist that may protect against motor neuron death in an ALS context. Moreover, GK11 presents a low intrinsic neurotoxicity and has already been used in two clinical trials for CNS lesions. In the present study, we investigated the influence of chronic administration of two doses of GK11 (0.1 and 1 mg/kg on the survival and the functional motor activity of hSOD1G93A mice, an animal model of ALS. Treatment started at early symptomatic age (60 days and was applied bi-weekly until the end stage of the disease. We first confirmed that functional alteration of locomotor activity was evident in the hSOD1G93A transgenic female mice by 60 days of age. A low dose of GK11 improved the survival of the mice by 4.3% and partially preserved body weight. Improved life span was associated with a delay in locomotor function impairment. Conversely, the high dose treatment worsened motor functions. These findings suggest that chronic administration of GK11beginning at early symptomatic stage may be beneficial for patients with ALS.

  8. Survival benefit with radium-223 dichloride in a mouse model of breast cancer bone metastasis.

    Science.gov (United States)

    Suominen, Mari I; Rissanen, Jukka P; Käkönen, Rami; Fagerlund, Katja M; Alhoniemi, Esa; Mumberg, Dominik; Ziegelbauer, Karl; Halleen, Jussi M; Käkönen, Sanna-Maria; Scholz, Arne

    2013-06-19

    Bone metastases are associated with increased morbidity and poor prognosis in breast cancer patients. Radium-223 dichloride is a calcium mimetic that localizes to bone, providing targeted therapy for skeletal metastasis. We investigated the mode of action of radium-223 dichloride using breast cancer cell, osteoclast, and osteoblast cultures as well as a mouse model of breast cancer bone metastasis. A single dose of radium-223 dichloride was used in three different settings mimicking the prevention or treatment of bone metastasis. Disease progression was monitored using fluorescence and radiographic imaging and histological analyses. The effect of radium-223 dichloride alone and in combination with doxorubicin or zoledronic acid on survival of mice was analyzed by Kaplan-Meier methods. All statistical tests used were two-sided. Radium-223 dichloride incorporated into bone matrix and inhibited proliferation of breast cancer cells and differentiation of osteoblasts and osteoclasts (all P values radium-223 dichloride prevented tumor-induced cachexia (0/14 vs 7/14 control mice) and decreased osteolysis by 56% and tumor growth by 43% (all P values Radium-223 dichloride induced double-strand DNA breaks in cancer cells in vivo. Finally, radium-223 dichloride extended survival as a monotherapy (29.2 days, 95% confidence interval [CI] = 26.6 to 31.8 days, P = .039) and in combination with zoledronic acid (31.4 days, 95% CI = 28.8 to 34.0 days, P = .004) or doxorubicin (31.5 days, 95% CI = 29.5 to 33.5 days, P radium-223 dichloride was administered in a preventive or micrometastatic setting. Our findings strongly support the development of radium-223 dichloride for the treatment of breast cancer patients with or at high risk of developing bone metastases.

  9. Glacial isostatic adjustment at the Laurentide ice sheet margin: Models and observations in the Great Lakes region

    Science.gov (United States)

    Braun, Alexander; Kuo, Chung-Yen; Shum, C. K.; Wu, Patrick; van der Wal, Wouter; Fotopoulos, Georgia

    2008-10-01

    Glacial Isostatic Adjustment (GIA) modelling in North America relies on relative sea level information which is primarily obtained from areas far away from the uplift region. The lack of accurate geodetic observations in the Great Lakes region, which is located in the transition zone between uplift and subsidence due to the deglaciation of the Laurentide ice sheet, has prevented more detailed studies of this former margin of the ice sheet. Recently, observations of vertical crustal motion from improved GPS network solutions and combined tide gauge and satellite altimetry solutions have become available. This study compares these vertical motion observations with predictions obtained from 70 different GIA models. The ice sheet margin is distinct from the centre and far field of the uplift because the sensitivity of the GIA process towards Earth parameters such as mantle viscosity is very different. Specifically, the margin area is most sensitive to the uppermost mantle viscosity and allows for better constraints of this parameter. The 70 GIA models compared herein have different ice loading histories (ICE-3/4/5G) and Earth parameters including lateral heterogeneities. The root-mean-square differences between the 6 best models and the two sets of observations (tide gauge/altimetry and GPS) are 0.66 and 1.57 mm/yr, respectively. Both sets of independent observations are highly correlated and show a very similar fit to the models, which indicates their consistent quality. Therefore, both data sets can be considered as a means for constraining and assessing the quality of GIA models in the Great Lakes region and the former margin of the Laurentide ice sheet.

  10. Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Cefiderocol, a Parenteral Siderophore Cephalosporin, for Dose Adjustment Based on Renal Function.

    Science.gov (United States)

    Katsube, Takayuki; Wajima, Toshihiro; Ishibashi, Toru; Arjona Ferreira, Juan Camilo; Echols, Roger

    2017-01-01

    Cefiderocol, a novel parenteral siderophore cephalosporin, exhibits potent efficacy against most Gram-negative bacteria, including carbapenem-resistant strains. Since cefiderocol is excreted primarily via the kidneys, this study was conducted to develop a population pharmacokinetics (PK) model to determine dose adjustment based on renal function. Population PK models were developed based on data for cefiderocol concentrations in plasma, urine, and dialysate with a nonlinear mixed-effects model approach. Monte-Carlo simulations were conducted to calculate the probability of target attainment (PTA) of fraction of time during the dosing interval where the free drug concentration in plasma exceeds the MIC (Tf>MIC) for an MIC range of 0.25 to 16 μg/ml. For the simulations, dose regimens were selected to compare cefiderocol exposure among groups with different levels of renal function. The developed models well described the PK of cefiderocol for each renal function group. A dose of 2 g every 8 h with 3-h infusions provided >90% PTA for 75% Tf>MIC for an MIC of ≤4 μg/ml for patients with normal renal function, while a more frequent dose (every 6 h) could be used for patients with augmented renal function. A reduced dose and/or extended dosing interval was selected for patients with impaired renal function. A supplemental dose immediately after intermittent hemodialysis was proposed for patients requiring intermittent hemodialysis. The PK of cefiderocol could be adequately modeled, and the modeling-and-simulation approach suggested dose regimens based on renal function, ensuring drug exposure with adequate bactericidal effect. Copyright © 2016 American Society for Microbiology.

  11. Stochastic Dynamic Model on the Consumption – Saving Decision for Adjusting Products and Services Supply According with Consumers` Attainability

    Directory of Open Access Journals (Sweden)

    Gabriela Prelipcean

    2014-02-01

    Full Text Available The recent crisis and turbulences have significantly changed the consumers’ behavior, especially through its access possibility and satisfaction, but also the new dynamic flexible adjustment of the supply of goods and services. The access possibility and consumer satisfaction should be analyzed in a broader context of corporate responsibility, including financial institutions. This contribution gives an answer to the current situation in Romania as an emerging country, strongly affected by the global crisis. Empowering producers and harmonize their interests with the interests of consumers really require a significant revision of the quantitative models used to study long-term consumption-saving behavior, with a new model, adapted to the current conditions in Romania in the post-crisis context. Based on the general idea of the model developed by Hai, Krueger, Postlewaite (2013 we propose a new way of exploiting the results considering the dynamics of innovative adaptation based on Brownian motion, but also the integration of the cyclicality concept, the stochastic shocks analyzed by Lèvy and extensive interaction with capital markets characterized by higher returns and volatility.

  12. A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria

    Directory of Open Access Journals (Sweden)

    Ezra Gayawan

    2013-06-01

    Full Text Available BACKGROUND The age at which childbearing begins influences the total number of children a woman bears throughout her reproductive period, in the absence of any active fertility control. For countries in sub-Saharan Africa where contraceptive prevalence rate is still low, younger ages at first birth tend to increase the number of children a woman will have thereby hindering the process of fertility decline. Research has also shown that early childbearing can endanger the health of the mother and her offspring, which can in turn lead to high child and maternal mortality. OBJECTIVE In this paper, an attempt was made to explore possible trends, geographical variation and determinants of timing of first birth in Nigeria, using the 1999 - 2008 Nigeria Demographic and Health Survey data sets. METHODS A structured additive survival model for continuous time data, an approach that simultaneously estimates the nonlinear effect of metrical covariates, fixed effects, spatial effects and smoothing parameters within a Bayesian context in one step is employed for all estimations. All analyses were carried out using BayesX - a software package for Bayesian modelling techniques. RESULTS Results from this paper reveal that variation in age at first birth in Nigeria is determined more by individual household than by community, and that substantial geographical variations in timing of first birth also exist. COMMENTS These findings can guide policymakers in identifying states or districts that are associated with significant risk of early childbirth, which can in turn be used in designing effective strategies and in decision making.

  13. Irreversible electroporation of the pancreas is feasible and safe in a porcine survival model.

    Science.gov (United States)

    Fritz, Stefan; Sommer, Christof M; Vollherbst, Dominik; Wachter, Miguel F; Longerich, Thomas; Sachsenmeier, Milena; Knapp, Jürgen; Radeleff, Boris A; Werner, Jens

    2015-07-01

    Use of thermal tumor ablation in the pancreatic parenchyma is limited because of the risk of pancreatitis, pancreatic fistula, or hemorrhage. This study aimed to evaluate the feasibility and safety of irreversible electroporation (IRE) in a porcine model. Ten pigs were divided into 2 study groups. In the first group, animals received IRE of the pancreatic tail and were killed after 60 minutes. In the second group, animals received IRE at the head of the pancreas and were followed up for 7 days. Clinical parameters, computed tomography imaging, laboratory results, and histology were obtained. All animals survived IRE ablation, and no cardiac adverse effects were noted. Sixty minutes after IRE, a hypodense lesion on computed tomography imaging indicated the ablation zone. None of the animals developed clinical signs of acute pancreatitis. Only small amounts of ascites fluid, with a transient increase in amylase and lipase levels, were observed, indicating that no pancreatic fistula occurred. This porcine model shows that IRE is feasible and safe in the pancreatic parenchyma. Computed tomography imaging reveals significant changes at 60 minutes after IRE and therefore might serve as an early indicator of therapeutic success. Clinical studies are needed to evaluate the efficacy of IRE in pancreatic cancer.

  14. 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.

  15. 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.

  16. Improving the Process of Adjusting the Parameters of Finite Element Models of Healthy Human Intervertebral Discs by the Multi-Response Surface Method.

    Science.gov (United States)

    Gómez, Fátima Somovilla; Lorza, Rubén Lostado; Bobadilla, Marina Corral; García, Rubén Escribano

    2017-09-21

    The kinematic behavior of models that are based on the finite element method (FEM) for modeling the human body depends greatly on an accurate estimate of the parameters that define such models. This task is complex, and any small difference between the actual biomaterial model and the simulation model based on FEM can be amplified enormously in the presence of nonlinearities. The current paper attempts to demonstrate how a combination of the FEM and the MRS methods with desirability functions can be used to obtain the material parameters that are most appropriate for use in defining the behavior of Finite Element (FE) models of the healthy human lumbar intervertebral disc (IVD). The FE model parameters were adjusted on the basis of experimental data from selected standard tests (compression, flexion, extension, shear, lateral bending, and torsion) and were developed as follows: First, three-dimensional parameterized FE models were generated on the basis of the mentioned standard tests. Then, 11 parameters were selected to define the proposed parameterized FE models. For each of the standard tests, regression models were generated using MRS to model the six stiffness and nine bulges of the healthy IVD models that were created by changing the parameters of the FE models. The optimal combination of the 11 parameters was based on three different adjustment criteria. The latter, in turn, were based on the combination of stiffness and bulges that were obtained from the standard test FE simulations. The first adjustment criteria considered stiffness and bulges to be equally important in the adjustment of FE model parameters. The second adjustment criteria considered stiffness as most important, whereas the third considered the bulges to be most important. The proposed adjustment methods were applied to a medium-sized human IVD that corresponded to the L3-L4 lumbar level with standard dimensions of width = 50 mm, depth = 35 mm, and height = 10 mm. Agreement between the

  17. Improving the Process of Adjusting the Parameters of Finite Element Models of Healthy Human Intervertebral Discs by the Multi-Response Surface Method

    Directory of Open Access Journals (Sweden)

    Fátima Somovilla Gómez

    2017-09-01

    Full Text Available The kinematic behavior of models that are based on the finite element method (FEM for modeling the human body depends greatly on an accurate estimate of the parameters that define such models. This task is complex, and any small difference between the actual biomaterial model and the simulation model based on FEM can be amplified enormously in the presence of nonlinearities. The current paper attempts to demonstrate how a combination of the FEM and the MRS methods with desirability functions can be used to obtain the material parameters that are most appropriate for use in defining the behavior of Finite Element (FE models of the healthy human lumbar intervertebral disc (IVD. The FE model parameters were adjusted on the basis of experimental data from selected standard tests (compression, flexion, extension, shear, lateral bending, and torsion and were developed as follows: First, three-dimensional parameterized FE models were generated on the basis of the mentioned standard tests. Then, 11 parameters were selected to define the proposed parameterized FE models. For each of the standard tests, regression models were generated using MRS to model the six stiffness and nine bulges of the healthy IVD models that were created by changing the parameters of the FE models. The optimal combination of the 11 parameters was based on three different adjustment criteria. The latter, in turn, were based on the combination of stiffness and bulges that were obtained from the standard test FE simulations. The first adjustment criteria considered stiffness and bulges to be equally important in the adjustment of FE model parameters. The second adjustment criteria considered stiffness as most important, whereas the third considered the bulges to be most important. The proposed adjustment methods were applied to a medium-sized human IVD that corresponded to the L3–L4 lumbar level with standard dimensions of width = 50 mm, depth = 35 mm, and height = 10 mm

  18. Clinical variables serve as prognostic factors in a model for survival from glioblastoma multiforme

    DEFF Research Database (Denmark)

    Michaelsen, Signe Regner; Christensen, Ib Jarle; Grunnet, Kirsten

    2013-01-01

    Although implementation of temozolomide (TMZ) as a part of primary therapy for glioblastoma multiforme (GBM) has resulted in improved patient survival, the disease is still incurable. Previous studies have correlated various parameters to survival, although no single parameter has yet been...

  19. Description and validation of a Markov model of survival for individuals free of cardiovascular disease that uses Framingham risk factors

    Directory of Open Access Journals (Sweden)

    Martin Chris

    2004-05-01

    Full Text Available Abstract Background Estimation of cardiovascular disease risk is increasingly used to inform decisions on interventions, such as the use of antihypertensives and statins, or to communicate the risks of smoking. Crude 10-year cardiovascular disease risk risks may not give a realistic view of the likely impact of an intervention over a lifetime and will underestimate of the risks of smoking. A validated model of survival to act as a decision aid in the consultation may help to address these problems. This study aims to describe the development of such a model for use with people free of cardiovascular disease and evaluates its accuracy against data from a United Kingdom cohort. Methods A Markov cycle tree evaluated using cohort simulation was developed utilizing Framingham estimates of cardiovascular risk, 1998 United Kingdom mortality data, the relative risk for smoking related non-cardiovascular disease risk and changes in systolic blood pressure and serum total cholesterol total cholesterol with age. The model's estimates of survival at 20 years for 1391 members of the Whickham survey cohort between the ages of 35 and 65 were compared with the observed survival at 20-year follow-up. Results The model estimate for survival was 75% and the observed survival was 75.4%. The correlation between estimated and observed survival was 0.933 over 39 subgroups of the cohort stratified by estimated survival, 0.992 for the seven 5-year age bands from 35 to 64, 0.936 for the ten 10 mmHg systolic blood pressure bands between 100 mmHg and 200 mmHg, and 0.693 for the fifteen 0.5 mmol/l total cholesterol bands between 3.0 and 10.0 mmol/l. The model significantly underestimated mortality in those people with a systolic blood pressure greater than or equal to 180 mmHg (p = 0.006. The average gain in life expectancy from the elimination of cardiovascular disease risk as a cause of death was 4.0 years for all the 35 year-old men in the sample (n = 24, and 1.8 years

  20. Description and validation of a Markov model of survival for individuals free of cardiovascular disease that uses Framingham risk factors.

    Science.gov (United States)

    Martin, Chris; Vanderpump, Mark; French, Joyce

    2004-05-24

    Estimation of cardiovascular disease risk is increasingly used to inform decisions on interventions, such as the use of antihypertensives and statins, or to communicate the risks of smoking. Crude 10-year cardiovascular disease risk risks may not give a realistic view of the likely impact of an intervention over a lifetime and will underestimate of the risks of smoking. A validated model of survival to act as a decision aid in the consultation may help to address these problems. This study aims to describe the development of such a model for use with people free of cardiovascular disease and evaluates its accuracy against data from a United Kingdom cohort. A Markov cycle tree evaluated using cohort simulation was developed utilizing Framingham estimates of cardiovascular risk, 1998 United Kingdom mortality data, the relative risk for smoking related non-cardiovascular disease risk and changes in systolic blood pressure and serum total cholesterol total cholesterol with age. The model's estimates of survival at 20 years for 1391 members of the Whickham survey cohort between the ages of 35 and 65 were compared with the observed survival at 20-year follow-up. The model estimate for survival was 75% and the observed survival was 75.4%. The correlation between estimated and observed survival was 0.933 over 39 subgroups of the cohort stratified by estimated survival, 0.992 for the seven 5-year age bands from 35 to 64, 0.936 for the ten 10 mmHg systolic blood pressure bands between 100 mmHg and 200 mmHg, and 0.693 for the fifteen 0.5 mmol/l total cholesterol bands between 3.0 and 10.0 mmol/l. The model significantly underestimated mortality in those people with a systolic blood pressure greater than or equal to 180 mmHg (p = 0.006). The average gain in life expectancy from the elimination of cardiovascular disease risk as a cause of death was 4.0 years for all the 35 year-old men in the sample (n = 24), and 1.8 years for all the 35 year-old women in the sample (n = 32

  1. 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.

  2. A Comparison Between Impedance Measured by a Commercial Analyzer and your Value Adjusted by a Theoretical Model in Body Composition Evaluation

    Science.gov (United States)

    2001-10-25

    suprailiac, thigh and calf sites, in order to get data to estimate FFM from antropometric parameters. The equations used for body composition estimate...Similarly, a scatter plot of the fat -free mass estimated from the new equation using Ztheo (FFMbia-adjusted) against our antropometric �gold...A COMPARISON BETWEEN IMPEDANCE MEASURED BY A COMMERCIAL ANALYZER AND YOUR VALUE ADJUSTED BY A THEORETICAL MODEL IN BODY COMPOSITION EVALUATION

  3. SERPINB3 in the chicken model of ovarian cancer: a prognostic factor for platinum resistance and survival in patients with epithelial ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Whasun Lim

    Full Text Available Serine protease inhibitors (SERPINs appear to be ubiquitously expressed in a variety of species and play important roles in pivotal physiological processes such as angiogenesis, immune responses, blood coagulation and fibronolysis. Of these, squamous cell carcinoma antigen 1 (SCCA1, also known as a SERPINB3, was first identified in squamous cell carcinoma tissue from the cervix of women. However, there is little known about the SERPINB3 expression in human epithelial ovarian cancer (EOC. Therefore, in the present study, we investigated the functional role of SERPINB3 gene in human EOC using chickens, the most relevant animal model. In 136 chickens, EOC was found in 10 (7.4%. SERPINB3 mRNA was induced in cancerous, but not normal ovaries of chickens (P<0.01, and it was abundant only in the glandular epithelium of cancerous ovaries of chickens. Further, several microRNAs, specifically miR-101, miR-1668 and miR-1681 were discovered to influence SERPINB3 expression via its 3'-UTR which suggests that post-transcriptional regulation influences SERPINB3 expression in chickens. SERPINB3 protein was localized predominantly to the glandular epithelium in cancerous ovaries of chickens, and it was abundant in the nucleus of both chicken and human ovarian cancer cell lines. In 109 human patients with EOC, 15 (13.8%, 66 (60.6% and 28 (25.7% patients showed weak, moderate and strong expression of SERPINB3 protein, respectively. Strong expression of SERPINB3 protein was a prognostic factor for platinum resistance (adjusted OR; odds ratio, 5.94; 95% Confidence Limits, 1.21-29.15, and for poor progression-free survival (PFS; adjusted HR; hazard ratio, 2.07; 95% CI; confidence interval, 1.03-4.41. Therefore, SERPINB3 may play an important role in ovarian carcinogenesis and be a novel biomarker for predicting platinum resistance and a poor prognosis for survival in patients with EOC.

  4. A new approach to the "apparent survival" problem: estimating true survival rates from mark-recapture studies.

    Science.gov (United States)

    Gilroy, James J; Virzi, Thomas; Boulton, Rebecca L; Lockwood, Julie L

    2012-07-01

    Survival estimates generated from live capture-mark-recapture studies may be negatively biased due to the permanent emigration of marked individuals from the study area. In the absence of a robust analytical solution, researchers typically sidestep this problem by simply reporting estimates using the term "apparent survival." Here, we present a hierarchical Bayesian multistate model designed to estimate true survival by accounting for predicted rates of permanent emigration. Initially we use dispersal kernels to generate spatial projections of dispersal probability around each capture location. From these projections, we estimate emigration probability for each marked individual and use the resulting values to generate bias-adjusted survival estimates from individual capture histories. When tested using simulated data sets featuring variable detection probabilities, survival rates, and dispersal patterns, the model consistently eliminated negative biases shown by apparent survival estimates from standard models. When applied to a case study concerning juvenile survival in the endangered Cape Sable Seaside Sparrow (Ammodramus maritimus mirabilis), bias-adjusted survival estimates increased more than twofold above apparent survival estimates. Our approach is applicable to any capture-mark-recapture study design and should be particularly valuable for organisms with dispersive juvenile life stages.

  5. 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.

  6. Club cell secretory protein improves survival in a murine obliterative bronchiolitis model.

    Science.gov (United States)

    Wendt, Christine; Tram, Kevin; Price, Andrew; England, Kristen; Stiehm, Andrew; Panoskaltsis-Mortari, Angela

    2013-11-01

    Club cell secretory protein (CCSP) is an indirect phospholipase A2 inhibitor with some immunosuppressive and antiproliferative properties that is expressed in bronchiolar Club cells. In our murine bone marrow transplant (BMT) model of obliterative bronchiolitis (OB), CCSP is diminished; however, its role is unknown. To determine the role of CCSP, B6 wild-type (WT) or CCSP-deficient (CCSP(-/-)) mice were lethally conditioned and given allogeneic bone marrow with a sublethal dose of allogeneic splenic T cells to induce OB. We found that CCSP(-/-) mice demonstrated a higher mortality following BMT-induced OB compared with WT mice. Mice were analyzed 60 days post-BMT for protein expression, pulmonary function, and histology. CCSP levels were reduced in WT mice with BMT-induced OB, and lower levels correlated to decreased lung compliance. CCSP(-/-) had a higher degree of injury and fibrosis as measured by hydroxy proline, along with an increased lung resistance and the inflammatory markers, leukotriene B4 and CXCL1. Replacement with recombinant intravenous CCSP partially reversed the weight loss and improved survival in the CCSP(-/-) mice. In addition, CCSP replacement improved histology and decreased inflammatory cells and markers. These findings indicate that CCSP has a regulatory role in OB and may have potential as a preventive therapy.

  7. Intranasal Oncolytic Virotherapy with CXCR4-Enhanced Stem Cells Extends Survival in Mouse Model of Glioma.

    Science.gov (United States)

    Dey, Mahua; Yu, Dou; Kanojia, Deepak; Li, Gina; Sukhanova, Madina; Spencer, Drew A; Pituch, Katatzyna C; Zhang, Lingjiao; Han, Yu; Ahmed, Atique U; Aboody, Karen S; Lesniak, Maciej S; Balyasnikova, Irina V

    2016-09-13

    The challenges to effective drug delivery to brain tumors are twofold: (1) there is a lack of non-invasive methods of local delivery and (2) the blood-brain barrier limits systemic delivery. Intranasal delivery of therapeutics to the brain overcomes both challenges. In mouse model of malignant glioma, we observed that a small fraction of intranasally delivered neural stem cells (NSCs) can migrate to the brain tumor site. Here, we demonstrate that hypoxic preconditioning or overexpression of CXCR4 significantly enhances the tumor-targeting ability of NSCs, but without altering their phenotype only in genetically modified NSCs. Modified NSCs deliver oncolytic virus to glioma more efficiently and extend survival of experimental animals in the context of radiotherapy. Our findings indicate that intranasal delivery of stem cell-based therapeutics could be optimized for future clinical applications, and allow for safe and repeated administration of biological therapies to brain tumors and other CNS disorders. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

  8. Positive end-expiratory pressure improves survival in a rodent model of cardiopulmonary resuscitation using high-dose epinephrine.

    LENUS (Irish Health Repository)

    McCaul, Conán

    2009-10-01

    Multiple interventions have been tested in models of cardiopulmonary resuscitation (CPR) to optimize drug use, chest compressions, and ventilation. None has studied the effects of positive end-expiratory pressure (PEEP) on outcome. We hypothesized that because PEEP can reverse pulmonary atelectasis, lower pulmonary vascular resistance, and potentially improve cardiac output, its use during CPR would increase survival.

  9. External validation of a model to predict the survival of patients presenting with a spinal epidural metastasis

    NARCIS (Netherlands)

    Bartels, R.H.M.A.; Feuth, T.; Rades, D.; Hedlund, R.; Villas, C.; Linden, Y. van der; Borm, W.; Kappelle, A.C.; Maazen, R.W. van der; Grotenhuis, J.A.; Verbeek, A.L.M.

    2011-01-01

    The surgical treatment of spinal metastases is evolving. The major problem is the selection of patients who may benefit from surgical treatment. One of the criteria is an expected survival of at least 3 months. A prediction model has been previously developed. The present study has been performed in

  10. Individual-tree basal area growth, survival, and total height models for upland hardwoods in the Boston Mountains of Arkansa

    Science.gov (United States)

    Paul A. Murphy; David L. Graney

    1988-01-01

    Models were developed for individual-tree basal area growth, survival, and total heights for different species of upland hardwoods in the Boston Mountains of north Arkansas. Data used were from 87 permanent plots located in an array of different sites and stand ages; the plots were thinned to different stocking levels and included unthinned controls. To test these...

  11. Anti-CD45 radioimmunotherapy using 211At with bone marrow transplantation prolongs survival in a disseminated murine leukemia model

    Energy Technology Data Exchange (ETDEWEB)

    Orozco, Johnnie J.; Back, Tom; Kenoyer, Aimee L.; Balkin, Ethan R.; Hamlin, Donald K.; Wilbur, D. Scott; Fisher, Darrell R.; Frayo, Shani; Hylarides, Mark; Green, Damian J.; Gopal, Ajay K.; Press, Oliver W.; Pagel, John M.

    2013-05-15

    Anti-CD45 Radioimmunotherapy using an Alpha-Emitting Radionuclide 211At Combined with Bone Marrow Transplantation Prolongs Survival in a Disseminated Murine Leukemia Model ABSTRACT Despite aggressive chemotherapy combined with hematopoietic cell transplant (HCT), many patients with acute myeloid leukemia (AML) relapse. Radioimmunotherapy (RIT) using antibodies (Ab) labeled primarily with beta-emitting radionuclides has been explored to reduce relapse.

  12. Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression.

    Science.gov (United States)

    Nematollahi, M; Akbari, R; Nikeghbalian, S; Salehnasab, C

    2017-01-01

    Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008-2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%; 85.9%, 76.9%, 97.3%, and 26.1%; and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.

  13. Multilevel modelling of clustered grouped survival data using Cox regression model: an application to ART dental restorations.

    Science.gov (United States)

    Wong, May C M; Lam, K F; Lo, Edward C M

    2006-02-15

    In some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was

  14. A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

    Science.gov (United States)

    Kossieris, Panagiotis; Makropoulos, Christos; Onof, Christian; Koutsoyiannis, Demetris

    2018-01-01

    Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 min time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-min time scale. The applicability of the methodology was assessed on a 5-min rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.

  15. Development and internal validation of a prognostic model to predict recurrence free survival in patients with adult granulosa cell tumors of the ovary

    NARCIS (Netherlands)

    van Meurs, Hannah S.; Schuit, Ewoud; Horlings, Hugo M.; van der Velden, Jacobus; van Driel, Willemien J.; Mol, Ben Willem J.; Kenter, Gemma G.; Buist, Marrije R.

    2014-01-01

    Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model. We

  16. Applying the Transactional Stress and Coping Model to Sickle Cell Disorder and Insulin-Dependent Diabetes Mellitus: Identifying Psychosocial Variables Related to Adjustment and Intervention

    Science.gov (United States)

    Hocking, Matthew C.; Lochman, John E.

    2005-01-01

    This review paper examines the literature on psychosocial factors associated with adjustment to sickle cell disease and insulin-dependent diabetes mellitus in children through the framework of the transactional stress and coping (TSC) model. The transactional stress and coping model views adaptation to a childhood chronic illness as mediated by…

  17. 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.

  18. Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

    Science.gov (United States)

    Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

    2017-10-01

    Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

  19. 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...

  20. Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients

    Directory of Open Access Journals (Sweden)

    J. Faradmal

    2016-01-01

    Full Text Available Introduction & Objective: Cox model is a common method to estimate survival and validity of the results is dependent on the proportional hazards assumption. K- Nearest neighbor is a nonparametric method for survival probability in heterogeneous communities. The purpose of this study was to compare the performance of k- nearest neighbor method (K-NN with Cox model. Materials & Methods: This retrospective cohort study was conducted in Hamadan Province, on 475 patients who had undergone kidney transplantation from 1994 to 2011. Data were extracted from patients’ medical records using a checklist. The duration of the time between kidney transplantation and rejection was considered as the surviv­al time. Cox model and k- nearest neighbor method were used for Data modeling. The prediction error Brier score was used to compare the performance models. Results: Out of 475 transplantations, 55 episodes of rejection occurred. 5, 10 and 15 year survival rates of transplantation were 91.70 %, 84.90% and 74.50%, respectively. The number of neighborhood optimized using cross validation method was 45. Cumulative Brier score of k-NN algorithm for t=5, 10 and 15 years were 0.003, 0.006 and 0.007, respectively. Cumulative Brier of score Cox model for t=5, 10 and 15 years were 0.036, 0.058 and 0.058, respectively. Prediction error of k-NN algorithm for t=5, 10 and 15 years was less than Cox model that shows that the k-NN method outperforms. Conclusions: The results of this study show that the predictions of KNN has higher accuracy than the Cox model when sample sizes and the number of predictor variables are high. Sci J Hamadan Univ Med Sci . 2016; 22 (4 :300-308

  1. 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.

  2. A semiparametric joint model for terminal trend of quality of life and survival in palliative care research.

    Science.gov (United States)

    Li, Zhigang; Frost, H R; Tosteson, Tor D; Zhao, Lihui; Liu, Lei; Lyons, Kathleen; Chen, Huaihou; Cole, Bernard; Currow, David; Bakitas, Marie

    2017-12-20

    Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented. Copyright © 2017 John Wiley & Sons, Ltd.

  3. A Model to Predict Psychological- and Health-Related Adjustment in Men with Prostate Cancer: The Role of Post Traumatic Growth, Physical Post Traumatic Growth, Resilience and Mindfulness

    Directory of Open Access Journals (Sweden)

    Deirdre M. J. Walsh

    2018-02-01

    Full Text Available Background: Post traumatic growth (PTG can be defined as positive change following a traumatic event. The current conceptualization of PTG encompasses five main dimensions, however, there is no dimension which accounts for the distinct effect of a physical trauma on PTG. The purpose of the present research was to test the role of PTG, physical post traumatic growth (PPTG, resilience and mindfulness in predicting psychological and health related adjustment.Method: Ethical approval was obtained from relevant institutional ethics committees. Participants (N = 241, who were at least 1 year post prostate cancer treatment, were invited to complete a battery of questionnaires either through an online survey or a paper and pencil package received in the post The sample ranged in age from 44 to 88 years (M = 64.02, SD = 7.76. Data were analysis using confirmatory factor analysis and structural equation modeling.Results: The physical post traumatic growth inventory (P-PTGI was used to evaluate the role of PPTG in predicting adjustment using structural equation modeling. P-PTGI predicted lower distress and improvement of quality of life, whereas conversely, the traditional PTG measure was linked with poor adjustment. The relationship between resilience and adjustment was found to be mediated by P-PTGI.Conclusion: Findings suggest the central role of PTG in the prostate cancer survivorship experience is enhanced by the inclusion of PPTG. Adjusting to a physical trauma such as illness (internal transgressor is unlike a trauma with an external transgressor as the physical trauma creates an entirely different framework for adjustment. The current study demonstrates the impact of PPTG on adjustment. This significantly adds to the theory of the development of PTG by highlighting the interplay of resilience with PTG, PPTG, and adjustment.

  4. Pharmacological Amelioration of Cone Survival and Vision in a Mouse Model for Leber Congenital Amaurosis.

    Science.gov (United States)

    Li, Songhua; Samardzija, Marijana; Yang, Zhihui; Grimm, Christian; Jin, Minghao

    2016-05-25

    improve cone survival and function in patients with LCA caused by RPE65 mutations. Using a mouse model carrying the most frequent LCA-associated mutation (R91W), we found that the mutant RPE65 underwent ubiquitination-dependent proteasomal degradation due to misfolding. Treatment of the mice with a chemical chaperone partially corrected stability, enzymatic activity, and subcellular localization of R91W RPE65, which was also accompanied by improvement of cone survival and vision. These findings identify an in vivo molecular pathogenic mechanism for R91W mutation and provide a feasible pharmacological approach that can delay vision loss in patients with RPE65 mutations. Copyright © 2016 the authors 0270-6474/16/365808-12$15.00/0.

  5. The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: a Monte Carlo Study.

    Science.gov (United States)

    Austin, Peter C; Reeves, Mathew J

    2013-03-01

    Hospital report cards, in which outcomes following the provision of medical or surgical care are compared across health care providers, are being published with increasing frequency. Essential to the production of these reports is risk-adjustment, which allows investigators to account for differences in the distribution of patient illness severity across different hospitals. Logistic regression models are frequently used for risk adjustment in hospital report cards. Many applied researchers use the c-statistic (equivalent to the area under the receiver operating characteristic curve) of the logistic regression model as a measure of the credibility and accuracy of hospital report cards. To determine the relationship between the c-statistic of a risk-adjustment model and the accuracy of hospital report cards. Monte Carlo simulations were used to examine this issue. We examined the influence of 3 factors on the accuracy of hospital report cards: the c-statistic of the logistic regression model used for risk adjustment, the number of hospitals, and the number of patients treated at each hospital. The parameters used to generate the simulated datasets came from analyses of patients hospitalized with a diagnosis of acute myocardial infarction in Ontario, Canada. The c-statistic of the risk-adjustment model had, at most, a very modest impact on the accuracy of hospital report cards, whereas the number of patients treated at each hospital had a much greater impact. The c-statistic of a risk-adjustment model should not be used to assess the accuracy of a hospital report card.

  6. 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.

  7. 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.

  8. 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.

  9. Moderate exercise training improves survival and ventricular remodeling in an animal model of left ventricular volume overload.

    Science.gov (United States)

    Lachance, Dominic; Plante, Eric; Bouchard-Thomassin, Andrée-Anne; Champetier, Serge; Roussel, Elise; Drolet, Marie-Claude; Arsenault, Marie; Couet, Jacques

    2009-09-01

    Exercise training has beneficial effects in patients with heart failure, although there is still no clear evidence that it may impact on their survival. There are no data regarding the effects of exercise in subjects with chronic left ventricular (LV) volume overload. Using a rat model of severe aortic valve regurgitation (AR), we studied the effects of long-term exercise training on survival, development of heart failure, and LV myocardial remodeling. One hundred sixty male adult rats were divided in 3 groups: sham sedentary (n=40), AR sedentary (n=80), and AR trained (n=40). Training consisted in treadmill running for up to 30 minutes, 5 times per week for 9 months, at a maximal speed of 20 m/minute. All sham-operated animals survived the entire course of the protocol. After 9 months, 65% of trained animals were alive compared with 46% of sedentary ones (P=0.05). Ejection fractions remained in the normal range (all above 60%) and LV masses between AR groups were similar. There was significantly less LV fibrosis in the trained group and lower LV filling pressures and improved echocardiographic diastolic parameters. Heart rate variability was also improved by exercise. Our data show that moderate endurance training is safe, does not increase the rate of developing heart failure, and most importantly, improves survival in this animal model of chronic LV volume overload. Exercise improved LV diastolic function, heart rate variability, and reduced myocardial fibrosis.

  10. Interleukin 7 immunotherapy improves host immunity and survival in a two-hit model of Pseudomonas aeruginosa pneumonia.

    Science.gov (United States)

    Shindo, Yuichiro; Fuchs, Anja G; Davis, Christopher G; Eitas, Tim; Unsinger, Jacqueline; Burnham, Carey-Ann D; Green, Jonathan M; Morre, Michel; Bochicchio, Grant V; Hotchkiss, Richard S

    2017-02-01

    Patients with protracted sepsis develop impaired immunity, which predisposes them to acquiring secondary infections. One of the most common and lethal secondary infections is Pseudomonas aeruginosa pneumonia. Immunoadjuvant therapy is a promising approach to reverse sepsis-induced immunosuppression and improve morbidity and mortality from secondary infections. Interleukin-7 is an immunoadjuvant that improves survival in clinically relevant animal models of polymicrobial peritonitis and in fungal sepsis. This study investigated the effect of recombinant human interleukin-7 (rhIL-7) on survival in a 2-hit model of sublethal cecal ligation and puncture followed by P. aeruginosa pneumonia. Potential immunologic mechanisms responsible for the rhIL-7 putative beneficial effect were also examined, focusing on IL-17, IL-22, IFN-γ, and TNF-α, cytokines that are critical in the control of sepsis and pulmonary Pseudomonas infections. Results showed that rhIL-7 was highly effective in preventing P. aeruginosa-induced death, i.e., 92% survival in rhIL-7-treated mice versus 56% survival in control mice. rhIL-7 increased absolute numbers of immune effector cells in lung and spleen and ameliorated the sepsis-induced loss of lung innate lymphoid cells (ILCs). rhIL-7 also significantly increased IL-17-, IFN-γ-, and TNF-α-producing lung ILCs and CD8 T cells as well as IFN-γ- and TNF-α-producing splenic T cell subsets and ILCs. Furthermore, rhIL-7 enhanced NF-κB and STAT3 signaling in lungs during sepsis and pneumonia. Given the high mortality associated with secondary P. aeruginosa pneumonia, the ability of rhIL-7 to improve immunity and increase survival in multiple animal models of sepsis, and the remarkable safety profile of rhIL-7, clinical trials with rhIL-7 should be considered. © Society for Leukocyte Biology.

  11. Isolation of human lymphatic malformation endothelial cells, their in vitro characterization and in vivo survival in a mouse xenograft model.

    Science.gov (United States)

    Lokmic, Zerina; Mitchell, Geraldine M; Koh Wee Chong, Nicholas; Bastiaanse, Jacqueline; Gerrand, Yi-Wen; Zeng, Yiping; Williams, Elizabeth D; Penington, Anthony J

    2014-01-01

    Human lymphatic vascular malformations (LMs), also known as cystic hygromas or lymphangioma, consist of multiple lymphatic endothelial cell-lined lymph-containing cysts. No animal model of this disease exists. To develop a mouse xenograft model of human LM, CD34(Neg)CD31(Pos) LM lymphatic endothelial cells (LM-LEC) were isolated from surgical specimens and compared to foreskin CD34(Neg)CD31(Pos) lymphatic endothelial cells (LECs). Cells were implanted into a mouse tissue engineering model for 1, 2 and 4 weeks. In vitro LM-LECs showed increased proliferation and survival under starvation conditions (P lymphatic malformations.

  12. Calculating the 30-day survival rate in acute myocardial infarction: should we use the treatment chain or the hospital catchment model?

    Directory of Open Access Journals (Sweden)

    Jan Norum

    2017-12-01

    Full Text Available Introduction: Acute myocardial infarction (AMI is a potentially deadly disease and significant efforts have been concentrated on improving hospital performance. A 30-day survival rate has become a key quality of care indicator. In Northern Norway, some patients undergoing AMI are directly transferred to the Regional Cardiac Intervention Center at the University Hospital of North Norway in Tromsø. Here, coronary angiography and percutaneous coronary intervention is performed. Consequently, local hospitals may be bypassed in the treatment chain, generating differences in case mix, and making the treatment chain model difficult to interpret. We aimed to compare the treatment chain model with an alternative based on patients’ place of living. Methods: Between 2013 and 2015, a total of 3,155 patients were registered in the Norwegian Patient Registry database. All patients were categorized according to their local hospital’s catchment area. The method of Guo-Romano, with an indifference interval of 0.02, was used to test whether a hospital was an outlier or not. We adjusted for age, sex, comorbidity, and number of prior hospitalizations. Conclusions: We revealed the 30-day AMI survival figure ranging between 88.0% and 93.5% (absolute difference 5.5% using the hospital catchment method. The treatment chain rate ranged between 86.0% and 94.0% (absolute difference 8.0%. The latter figure is the one published as the National Quality of Care Measure in Norway. Local hospitals may get negative attention even though their catchment area is well served. We recommend the hospital catchment method as the first choice when measuring equality of care.

  13. 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.

  14. 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

  15. Analysis of case-parent trios for imprinting effect using a loglinear model with adjustment for sex-of-parent-specific transmission ratio distortion.

    Science.gov (United States)

    Huang, Lam Opal; Infante-Rivard, Claire; Labbe, Aurélie

    2017-08-01

    Transmission ratio distortion (TRD) is a phenomenon where parental transmission of disease allele to the child does not follow the Mendelian inheritance ratio. TRD occurs in a sex-of-parent-specific or non-sex-of-parent-specific manner. An offset computed from the transmission probability of the minor allele in control-trios can be added to the loglinear model to adjust for TRD. Adjusting the model removes the inflation in the genotype relative risk (RR) estimate and Type 1 error introduced by non-sex-of-parent-specific TRD. We now propose to further extend this model to estimate an imprinting parameter. Some evidence suggests that more than 1% of all mammalian genes are imprinted. In the presence of imprinting, for example, the offspring inheriting an over-transmitted disease allele from the parent with a higher expression level in a neighboring gene is over-represented in the sample. TRD mechanisms such as meiotic drive and gametic competition occur in a sex-of-parent-specific manner. Therefore, sex-of-parent-specific TRD (ST) leads to over-representation of maternal or paternal alleles in the affected child. As a result, ST may bias the imprinting effect when present in the sample. We propose a sex-of-parent-specific transmission offset in adjusting the loglinear model to account for ST. This extended model restores the correct RR estimates for child and imprinting effects, adjusts for inflation in Type 1 error, and improves performance on sensitivity and specificity compared to the original model without ST offset. We conclude that to correctly interpret the association signal of an imprinting effect, adjustment for ST is necessary to ensure valid conclusions.

  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. Modeling Intercellular Communication as a Survival Strategy of Cancer Cells: An In Silico Approach on a Flexible Bioinformatics Framework

    OpenAIRE

    Maura Cárdenas-García; Pedro P. González-Pérez; Sara Montagna; Oscar Sánchez Cortés; Elena Hernández Caballero

    2016-01-01

    Intercellular communication is very important for cell development and allows a group of cells to survive as a population. Cancer cells have a similar behavior, presenting the same mechanisms and characteristics of tissue formation. In this article, we model and simulate the formation of different communication channels that allow an interaction between two cells. This is a first step in order to simulate in the future processes that occur in healthy tissue when normal cells surround a cancer...

  18. Gamma Knife Surgery as Monotherapy with Clinically Relevant Doses Prolongs Survival in a Human GBM Xenograft Model

    Directory of Open Access Journals (Sweden)

    Bente Sandvei Skeie

    2013-01-01

    Full Text Available Object. Gamma knife surgery (GKS may be used for recurring glioblastomas (GBMs. However, patients have then usually undergone multimodal treatment, which makes it difficult to specifically validate GKS independent of established treatments. Thus, we developed an experimental brain tumor model to assess the efficacy and radiotoxicity associated with GKS. Methods. GBM xenografts were implanted intracerebrally in nude rats, and engraftment was confirmed with MRI. The rats were allocated to GKS, with margin doses of 12 Gy or 18 Gy, or to no treatment. Survival time was recorded, tumor sections were examined, and radiotoxicity was evaluated in a behavioral open field test. Results. In the first series, survival from the time of implantation was 96 days in treated rats and 72 days in controls (P<0.001. In a second experiment, survival was 72 days in the treatment group versus 54 days in controls (P<0.006. Polynuclear macrophages and fibrosis was seen in groups subjected to GKS. Untreated rats with GBM xenografts displayed less mobility than GKS-treated animals in the open field test 4 weeks after treatment (P=0.04. Conclusion. GKS administered with clinically relevant doses prolongs survival in rats harboring GBM xenografts, and the associated toxicity is mild.

  19. 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

  20. Genetic relationship of discrete-time survival with fertility and production in dairy cattle using bivariate models

    Directory of Open Access Journals (Sweden)

    Alenda Rafael

    2007-07-01

    Full Text Available Abstract Bivariate analyses of functional longevity in dairy cattle measured as survival to next lactation (SURV with milk yield and fertility traits were carried out. A sequential threshold-linear censored model was implemented for the analyses of SURV. Records on 96 642 lactations from 41 170 cows were used to estimate genetic parameters, using animal models, for longevity, 305 d-standardized milk production (MY305, days open (DO and number of inseminations to conception (INS in the Spanish Holstein population; 31% and 30% of lactations were censored for DO and INS, respectively. Heritability estimates for SURV and MY305 were 0.11 and 0.27 respectively; while heritability estimates for fertility traits were lower (0.07 for DO and 0.03 for INS. Antagonist genetic correlations were estimated between SURV and fertility (-0.78 and -0.54 for DO and INS, respectively or production (-0.53 for MY305, suggesting reduced functional longevity with impaired fertility and increased milk production. Longer days open seems to affect survival more than increased INS. Also, high productive cows were more problematic, less functional and more liable to being culled. The results suggest that the sequential threshold model is a method that might be considered at evaluating genetic relationship between discrete-time survival and other traits, due to its flexibility.

  1. Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

    Science.gov (United States)

    Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

    2017-11-01

    Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  2. 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.

  3. 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.

  4. Recombinant human erythropoietin increases survival and reduces neuronal apoptosis in a murine model of cerebral malaria

    DEFF Research Database (Denmark)

    Wiese, Lothar; Hempel, Casper; Penkowa, Milena

    2008-01-01

    BACKGROUND: Cerebral malaria (CM) is an acute encephalopathy with increased pro-inflammatory cytokines, sequestration of parasitized erythrocytes and localized ischaemia. In children CM induces cognitive impairment in about 10% of the survivors. Erythropoietin (Epo) has - besides of its well known...... with recombinant human Epo (rhEpo; 50-5000 U/kg/OD, i.p.) at different time points. The effect on survival was measured. Brain pathology was investigated by TUNEL (Terminal deoxynucleotidyl transferase (TdT)-mediated deoxyuridine triphosphate (dUTP)-digoxigenin nick end labelling), as a marker of apoptosis. Gene...... expression in brain tissue was measured by real time PCR. RESULTS: Treatment with rhEpo increased survival in mice with CM in a dose- and time-dependent manner and reduced apoptotic cell death of neurons as well as the expression of pro-inflammatory cytokines in the brain. This neuroprotective effect...

  5. Survival in Women with NSCLC

    Science.gov (United States)

    Katcoff, Hannah; Wenzlaff, Angela S.; Schwartz, Ann G.

    2014-01-01

    Introduction Although lung cancer is the leading cause of cancer death in women, few studies have investigated the hormonal influence on survival after a lung cancer diagnosis and results have been inconsistent. We evaluated the role of reproductive and hormonal factors in predicting overall survival in women with non–small-cell lung cancer (NSCLC). Methods Population-based lung cancer cases diagnosed between November 1, 2001 and October 31, 2005 were identified through the Metropolitan Detroit Surveillance, Epidemiology, and End Results Registry. Interview and follow-up data were collected for 485 women. Cox proportional hazard regression models were used to determine hazard ratios (HRs) for death after an NSCLC diagnosis associated with reproductive and hormonal variables. Results Use of hormone therapy (HT) was associated with improved survival (HR, 0.69; 95% confidence interval, 0.54–0.89), adjusting for stage, surgery, radiation, education level, pack-years of smoking, age at diagnosis, race, and a multiplicative interaction between stage and radiation. No other reproductive or hormonal factor was associated with survival after an NSCLC diagnosis. Increased duration of HT use before the lung cancer diagnosis (132 months or longer) was associated with improved survival (HR, 0.54; 95% confidence interval, 0.37–0.78), and this finding remained significant in women taking either estrogen alone or progesterone plus estrogen, never smokers, and smokers. Conclusion These findings suggest that HT use, in particular use of estrogen plus progesterone, and long-term HT use are associated with improved survival of NSCLC. PMID:24496005

  6. Tumor Cells Growth and Survival Time with the Ketogenic Diet in Animal Models: A Systematic Review.

    Science.gov (United States)

    Khodadadi, Soheila; Sobhani, Nafiseh; Mirshekar, Somaye; Ghiasvand, Reza; Pourmasoumi, Makan; Miraghajani, Maryam; Dehsoukhteh, Somayeh Shahraki

    2017-01-01

    Recently, interest in targeted cancer therapies via metabolic pathways has been renewed with the discovery that many tumors become dependent on glucose uptake during anaerobic glycolysis. Also the inability of ketone bodies metabolization due to various deficiencies in mitochondrial enzymes is the major metabolic changes discovered in malignant cells. Therefore, administration of a ketogenic diet (KD) which is based on high in fat and low in carbohydrates might inhibit tumor growth and provide a rationale for therapeutic strategies. So, we conducted this systematic review to assess the effects of KD on the tumor cells growth and survival time in animal studies. All databases were searched from inception to November 2015. We systematically searched the PubMed, Scopus, Google Scholars, Science Direct and Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. To assess the quality of included studies we used SYRCLE's RoB tool. 268 articles were obtained from databases by primary search. Only 13 studies were eligible according to inclusion criteria. From included studies, 9 articles indicate that KD had a beneficial effect on tumor growth and survival time. Tumor types were included pancreatic, prostate, gastric, colon, brain, neuroblastoma and lung cancers. In conclusions, although studies in this field are rare and inconsistence, recent findings have demonstrated that KD can potentially inhibit the malignant cell growth and increase the survival time. Because of differences physiology between animals and humans, future studies in cancer patients treated with a KD are needed.

  7. Tumor cells growth and survival time with the ketogenic diet in animal models: A systematic review

    Directory of Open Access Journals (Sweden)

    Soheila Khodadadi

    2017-01-01

    Full Text Available Recently, interest in targeted cancer therapies via metabolic pathways has been renewed with the discovery that many tumors become dependent on glucose uptake during anaerobic glycolysis. Also the inability of ketone bodies metabolization due to various deficiencies in mitochondrial enzymes is the major metabolic changes discovered in malignant cells. Therefore, administration of a ketogenic diet (KD which is based on high in fat and low in carbohydrates might inhibit tumor growth and provide a rationale for therapeutic strategies. So, we conducted this systematic review to assess the effects of KD on the tumor cells growth and survival time in animal studies. All databases were searched from inception to November 2015. We systematically searched the PubMed, Scopus, Google Scholars, Science Direct and Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. To assess the quality of included studies we used SYRCLE's RoB tool. 268 articles were obtained from databases by primary search. Only 13 studies were eligible according to inclusion criteria. From included studies, 9 articles indicate that KD had a beneficial effect on tumor growth and survival time. Tumor types were included pancreatic, prostate, gastric, colon, brain, neuroblastoma and lung cancers. In conclusions, although studies in this field are rare and inconsistence, recent findings have demonstrated that KD can potentially inhibit the malignant cell growth and increase the survival time. Because of differences physiology between animals and humans, future studies in cancer patients treated with a KD are needed.

  8. 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

  9. Survival After Retirement.

    Science.gov (United States)

    Holloway, Clark; Youngblood, Stuart A.

    1986-01-01

    Examined survival rates after retirement in a large corporation. A regression analysis was performed to control for age, sex, job status, and type of work differences that may influence longevity. Short-term suvivors seemed to undergo a different adjustment process than long-term survivors. (Author/ABL)

  10. Adjustable Autonomy Testbed

    Science.gov (United States)

    Malin, Jane T.; Schrenkenghost, Debra K.

    2001-01-01

    The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

  11. 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

  12. EGFR inhibitor erlotinib delays disease progression but does not extend survival in the SOD1 mouse model of ALS.

    Directory of Open Access Journals (Sweden)

    Claire E Le Pichon

    Full Text Available Amyotrophic lateral sclerosis (ALS is a fatal neurodegenerative disease that causes progressive paralysis due to motor neuron death. Several lines of published evidence suggested that inhibition of epidermal growth factor receptor (EGFR signaling might protect neurons from degeneration. To test this hypothesis in vivo, we treated the SOD1 transgenic mouse model of ALS with erlotinib, an EGFR inhibitor clinically approved for oncology indications. Although erlotinib failed to extend ALS mouse survival it did provide a modest but significant delay in the onset of multiple behavioral measures of disease progression. However, given the lack of protection of motor neuron synapses and the lack of survival extension, the small benefits observed after erlotinib treatment appear purely symptomatic, with no modification of disease course.

  13. Survival Old Model Tamping on Bugis House in Kampong of Bunne Regency of Soppeng South Sulawesi Indonesia

    Science.gov (United States)

    Abidah, Andi

    2017-10-01

    Tamping is space circulation from terrace to inside home and also as space for sitting space for low rank social community. Position tamping is one of side of main house. The floor of tamping slightly low than main house floor, this model has seldom found today which community more refer on new tamping model. The new model of tamping today, the same level on main house floor. Even new Bugis house model without tamping. Old model house use tamping but the tamping and watangpola ha the same floor level. This model consists of four modules which three modules on main house and one module tamping. In the past, old model of tamping is different level floor between watangpola and tamping floor now this tamping floor of old Bugis house model gone the same level of watangpola. While new model called eppa-eppa house, did not use tamping. Community in Kampung Bunne is till survive on old model of tamping on their house although several house has change its tamping like community applied now. This model is still found around 45 house of total number of house in the kampung. This study will explore applying old model of tamping of Bugis house in kampong Bunne Regency of Soppeng South Sulawesi. Qualitative research is used on this study. The study was developed base in sketch, photograph and interview.

  14. Psychosocial adjustment following ostomy.

    Science.gov (United States)

    Follick, M J; Smith, T W; Turk, D C

    1984-01-01

    Ostomy patients have been identified as a chronic illness population frequently experiencing adjustment difficulties. The present study, based on the biopsychosocial model (Engel, 1977) of chronic illness, examined a range of post-surgical adjustment difficulties in a sample of 131 ostomy patients. The patient population reported experiencing a significant number of technical, emotional, social, marital/family, and sexual difficulties post-surgically. Technical difficulties were associated with impaired emotional, social, and marital/family functioning. Emotional difficulties were also associated with problematic social, marital/family adjustment, and impaired sexual functioning. Technical problems, emotional difficulties, and social problems were all associated with the patient's perception of having received inadequate preparatory information. Marital/family and sexual maladjustment, on the other hand, were associated with low levels of perceived social support. The results of this investigation are interpreted as supporting the biopsychosocial model of chronic illness, and the clinical implications of these findings are discussed as well as their relation to previous research on adjustment to stressful medical procedures.

  15. Palliative resection of the primary tumor is associated with improved overall survival in incurable stage IV colorectal cancer: A nationwide population-based propensity-score adjusted study in the Netherlands

    NARCIS (Netherlands)

    Lam-Boer, J. 't; Geest, L.G. van der; Verhoef, C.; Elferink, M.E.; Koopman, M.; Wilt, J.H.W. de

    2016-01-01

    As the value of palliative primary tumor resection in stage IV colorectal cancer (CRC) is still under debate, the purpose of this population-based study was to investigate if palliative primary tumor resection as the initial treatment after diagnosis was associated with improved overall survival.

  16. Palliative resection of the primary tumor is associated with improved overall survival in incurable stage IV colorectal cancer : A nationwide population-based propensity-score adjusted study in the Netherlands

    NARCIS (Netherlands)

    't Lam-Boer, Jorine; Van der Geest, Lydia G; Verhoef, Cees; Elferink, Marloes E; Koopman, Miriam; de Wilt, Johannes H

    2016-01-01

    As the value of palliative primary tumor resection in stage IV colorectal cancer (CRC) is still under debate, the purpose of this population-based study was to investigate if palliative primary tumor resection as the initial treatment after diagnosis was associated with improved overall survival.

  17. Shortwave radiative forcing, rapid adjustment, and feedback to the surface by sulfate geoengineering: analysis of the Geoengineering Model Intercomparison Project G4 scenario

    Science.gov (United States)

    Kashimura, Hiroki; Abe, Manabu; Watanabe, Shingo; Sekiya, Takashi; Ji, Duoying; Moore, John C.; Cole, Jason N. S.; Kravitz, Ben

    2017-03-01

    This study evaluates the forcing, rapid adjustment, and feedback of net shortwave radiation at the surface in the G4 experiment of the Geoengineering Model Intercomparison Project by analysing outputs from six participating models. G4 involves injection of 5 Tg yr-1 of SO2, a sulfate aerosol precursor, into the lower stratosphere from year 2020 to 2069 against a background scenario of RCP4.5. A single-layer atmospheric model for shortwave radiative transfer is used to estimate the direct forcing of solar radiation management (SRM), and rapid adjustment and feedbacks from changes in the water vapour amount, cloud amount, and surface albedo (compared with RCP4.5). The analysis shows that the globally and temporally averaged SRM forcing ranges from -3.6 to -1.6 W m-2, depending on the model. The sum of the rapid adjustments and feedback effects due to changes in the water vapour and cloud amounts increase the downwelling shortwave radiation at the surface by approximately 0.4 to 1.5 W m-2 and hence weaken the effect of SRM by around 50 %. The surface albedo changes decrease the net shortwave radiation at the surface; it is locally strong (˜ -4 W m-2) in snow and sea ice melting regions, but minor for the global average. The analyses show that the results of the G4 experiment, which simulates sulfate geoengineering, include large inter-model variability both in the direct SRM forcing and the shortwave rapid adjustment from change in the cloud amount, and imply a high uncertainty in modelled processes of sulfate aerosols and clouds.

  18. Determination of a Change Point in the Age at Diagnosis of Breast Cancer Using a Survival Model.

    Science.gov (United States)

    Abdollahi, Mahbubeh; Hajizadeh, Ebrahim; Baghestani, Ahmad Reza; Haghighat, Shahpar

    2016-01-01

    Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

  19. 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

  20. Hydraulic adjustments underlying drought resistance of Pinus halepensis

    National Research Council Canada - National Science Library

    Klein, Tamir; Cohen, Shabtai; Yakir, Dan; Tognetti, Roberto

    2011-01-01

    .... Our objective was to investigate under controlled conditions the hydraulic adjustments underlying the observed ability of Pinus halepensis to survive seasonal drought under semi-arid conditions...

  1. On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

    Science.gov (United States)

    Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

    2017-10-01

    With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

  2. Correction: Utility of a single adjusting compartment: a novel methodology for whole body physiologically-based pharmacokinetic modelling

    Directory of Open Access Journals (Sweden)

    Hori Wataru

    2009-12-01

    Full Text Available Abstract After our work was published, we found that some of the terms in the equations were incorrect and that there were some typographical errors in the abbreviations. In the section 'Single adjusting compartment' in Materials and Methods, VS should be VSAC. In the last paragraph of Results, QSAC should be QSAC. The correct equations are included in this article. These corrections will not affect the results of this study.

  3. Adjusting eptifibatide doses for renal impairment: a model of dosing agreement among various methods of estimating creatinine clearance.

    Science.gov (United States)

    Healy, Martha F; Speroni, Karen Gabel; Eugenio, Kenneth R; Murphy, Patricia M

    2012-04-01

    Because of the renal elimination and increased risk for bleeding events at supratherapeutic doses of eptifibatide, the manufacturer recommends dosing adjustment in patients with renal dysfunction. Methods commonly used to estimate renal dysfunction in hospital settings may be inconsistent with those studied and recommended by the manufacturer. To compare hypothetical renal dosing adjustments of eptifibatide using both the recommended method and several other commonly used formulas for estimating kidney function. Sex, age, weight, height, serum creatinine, and estimated glomerular filtration rate (eGFR) were obtained retrospectively from the records of patients who received eptifibatide during a 12-month period. Renal dosing decisions were determined for each patient based on creatinine clearance (CrCl) estimates via the Cockcroft-Gault formula (CG) with actual body weight (ABW), ideal body weight (IBW) or adjusted weight (ADJW), and eGFR from the Modification of Diet in Renal Disease formula. Percent agreement and Cohen κ were calculated comparing dosing decisions for each formula to the standard CG-ABW. In this analysis of 179 patients, percent agreement as compared to CG-ABW varied (CG-IBW: 90.50%, CG-ADJW: 95.53%, and eGFR: 93.30%). All κ coefficients were categorized as good. In the 20% of patients receiving an adjusted dose by any of the methods, 68.6% could have received a dose different from that determined using the CG-ABW formula. In the patients with renal impairment (CrCl eptifibatide doses in patients with renal impairment has led to increased bleeding events, practitioners may be inclined to err on the side of caution. However, studies have shown that suboptimal doses of eptifibatide lead to suboptimal outcomes. Therefore, correct dosing of eptifibatide is important to both patient safety and efficacy.

  4. Intracranial AAV-IFN-β gene therapy eliminates invasive xenograft glioblastoma and improves survival in orthotopic syngeneic murine model.

    Science.gov (United States)

    GuhaSarkar, Dwijit; Neiswender, James; Su, Qin; Gao, Guangping; Sena-Esteves, Miguel

    2017-02-01

    The highly invasive property of glioblastoma (GBM) cells and genetic heterogeneity are largely responsible for tumor recurrence after the current standard-of-care treatment and thus a direct cause of death. Previously, we have shown that intracranial interferon-beta (IFN-β) gene therapy by locally administered adeno-associated viral vectors (AAV) successfully treats noninvasive orthotopic glioblastoma models. Here, we extend these findings by testing this approach in invasive human GBM xenograft and syngeneic mouse models. First, we show that a single intracranial injection of AAV encoding human IFN-β eliminates invasive human GBM8 tumors and promotes long-term survival. Next, we screened five AAV-IFN-β vectors with different promoters to drive safe expression of mouse IFN-β in the brain in the context of syngeneic GL261 tumors. Two AAV-IFN-β vectors were excluded due to safety concerns, but therapeutic studies with the other three vectors showed extensive tumor cell death, activation of microglia surrounding the tumors, and a 56% increase in median survival of the animals treated with AAV/P2-Int-mIFN-β vector. We also assessed the therapeutic effect of combining AAV-IFN-β therapy with temozolomide (TMZ). As TMZ affects DNA replication, an event that is crucial for second-strand DNA synthesis of single-stranded AAV vectors before active transcription, we tested two TMZ treatment regimens. Treatment with TMZ prior to AAV-IFN-β abrogated any benefit from the latter, while the reverse order of treatment doubled the median survival compared to controls. These studies demonstrate the therapeutic potential of intracranial AAV-IFN-β therapy in a highly migratory GBM model as well as in a syngeneic mouse model and that combination with TMZ is likely to enhance its antitumor potency. © 2016 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

  5. A Guide to Making Stochastic and Single Point Predictions using the Cold Exposure Survival Model (CESM)

    Science.gov (United States)

    2008-01-01

    connaissances nouvelles qui sont acquises. Son avantage par rapport à d’autres modèles de survie tient au fait qu’il peut être ajusté en fonction de la...l’immersion partielle ou totale dans l’eau. L’ajout de la fonction stochastique a permis d’améliorer les capacités prédictives en calculant la...the point at which Functional Time (FT) and Survival Time (ST) are attained. FT is defined by the deep body temperature when cognitive functions

  6. 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.

  7. Risk-adjustment models for heart failure patients' 30-day mortality and readmission rates: the incremental value of clinical data abstracted from medical charts beyond hospital discharge record.

    Science.gov (United States)

    Lenzi, Jacopo; Avaldi, Vera Maria; Hernandez-Boussard, Tina; Descovich, Carlo; Castaldini, Ilaria; Urbinati, Stefano; Di Pasquale, Giuseppe; Rucci, Paola; Fantini, Maria Pia

    2016-09-06

    Hospital discharge records (HDRs) are routinely used to assess outcomes of care and to compare hospital performance for heart failure. The advantages of using clinical data from medical charts to improve risk-adjustment models remain controversial. The aim of the present study was to evaluate the additional contribution of clinical variables to HDR-based 30-day mortality and readmission model