Volumetric and MGMT parameters in glioblastoma patients: Survival analysis

International Nuclear Information System (INIS)

Iliadis, Georgios; Kotoula, Vassiliki; Chatzisotiriou, Athanasios; Televantou, Despina; Eleftheraki, Anastasia G; Lambaki, Sofia; Misailidou, Despina; Selviaridis, Panagiotis; Fountzilas, George

2012-01-01

In this study several tumor-related volumes were assessed by means of a computer-based application and a survival analysis was conducted to evaluate the prognostic significance of pre- and postoperative volumetric data in patients harboring glioblastomas. In addition, MGMT (O 6 -methylguanine methyltransferase) related parameters were compared with those of volumetry in order to observe possible relevance of this molecule in tumor development. We prospectively analyzed 65 patients suffering from glioblastoma (GBM) who underwent radiotherapy with concomitant adjuvant temozolomide. For the purpose of volumetry T1 and T2-weighted magnetic resonance (MR) sequences were used, acquired both pre- and postoperatively (pre-radiochemotherapy). The volumes measured on preoperative MR images were necrosis, enhancing tumor and edema (including the tumor) and on postoperative ones, net-enhancing tumor. Age, sex, performance status (PS) and type of operation were also included in the multivariate analysis. MGMT was assessed for promoter methylation with Multiplex Ligation-dependent Probe Amplification (MLPA), for RNA expression with real time PCR, and for protein expression with immunohistochemistry in a total of 44 cases with available histologic material. In the multivariate analysis a negative impact was shown for pre-radiochemotherapy net-enhancing tumor on the overall survival (OS) (p = 0.023) and for preoperative necrosis on progression-free survival (PFS) (p = 0.030). Furthermore, the multivariate analysis confirmed the importance of PS in PFS and OS of patients. MGMT promoter methylation was observed in 13/23 (43.5%) evaluable tumors; complete methylation was observed in 3/13 methylated tumors only. High rate of MGMT protein positivity (> 20% positive neoplastic nuclei) was inversely associated with pre-operative tumor necrosis (p = 0.021). Our findings implicate that volumetric parameters may have a significant role in the prognosis of GBM patients. Furthermore

Method for statistical data analysis of multivariate observations

CERN Document Server

Gnanadesikan, R

1997-01-01

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

Outcome predictors in the management of intramedullary classic ependymoma: An integrative survival analysis.

Science.gov (United States)

Wang, Yinqing; Cai, Ranze; Wang, Rui; Wang, Chunhua; Chen, Chunmei

2018-06-01

This is a retrospective study.The aim of this study was to illustrate the survival outcomes of patients with classic ependymoma (CE) and identify potential prognostic factors.CE is the most common category of spinal ependymomas, but few published studies have discussed predictors of the survival outcome.A Boolean search of the PubMed, Embase, and OVID databases was conducted by 2 investigators independently. The objects were intramedullary grade II ependymoma according to 2007 WHO classification. Univariate Kaplan-Meier analysis and Log-Rank tests were performed to identify variables associated with progression-free survival (PFS) or overall survival (OS). Multivariate Cox regression was performed to assess hazard ratios (HRs) with 95% confidence intervals (95% CIs). Statistical analysis was performed by SPSS version 23.0 (IBM Corp.) with statistical significance defined as P analysis showed that patients who had undergone total resection (TR) had better PFS and OS than those with subtotal resection (STR) and biopsy (P = .002, P = .004, respectively). Within either univariate or multivariate analysis (P = .000, P = .07, respectively), histological type was an independent prognostic factor for PFS of CE [papillary type: HR 0.002, 95% CI (0.000-0.073), P = .001, tanycytic type: HR 0.010, 95% CI (0.000-0.218), P = .003].It was the first integrative analysis of CE to elucidate the correlation between kinds of factors and prognostic outcomes. Definite histological type and safely TR were foundation of CE's management. 4.

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

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

2017-12-01

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

Matrix-based introduction to multivariate data analysis

CERN Document Server

Adachi, Kohei

2016-01-01

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

Marital status independently predicts testis cancer survival--an analysis of the SEER database.

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Abern, Michael R; Dude, Annie M; Coogan, Christopher L

2012-01-01

Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P married status (HR 0.60, P married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.

Multivariate Regression Analysis and Slaughter Livestock,

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AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Multivariate refined composite multiscale entropy analysis

International Nuclear Information System (INIS)

Humeau-Heurtier, Anne

2016-01-01

Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

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Elza Jurun

2012-12-01

Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

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Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Prognostic factors in nodular lymphomas: a multivariate analysis based on the Princess Margaret Hospital experience

International Nuclear Information System (INIS)

Gospodarowicz, M.K.; Bush, R.S.; Brown, T.C.; Chua, T.

1984-01-01

A total of 1,394 patients with non-Hodgkin's lymphoma were treated at the Princess Margaret Hospital between January 1, 1967 and December 31, 1978. Overall actuarial survival of 525 patients with nodular lymphomas was 40% at 12 years; survival of patients with localized (Stage I and III) nodular lymphomas treated with radical radiation therapy was 58%. Significant prognostic factors defined by multivariate analysis included patient's age, stage, histology, tumor bulk, and presence of B symptoms. By combining prognostic factors, distinct prognostic groups have been identified within the overall population. Patients with Stage I and II disease, small or medium bulk, less than 70 years of age achieved 92% 12 year actuarial survival and a 73% relapse-free rate in 12 years of follow-up. These patients represent groups highly curable with irradiation

Multivariate analysis of 2-DE protein patterns - Practical approaches

DEFF Research Database (Denmark)

Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

2007-01-01

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

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

Interfraction interval does not affect survival of patients with non-small cell lung cancer treated with hyperfractionated radiotherapy with/without chemotherapy: a multivariate analysis of 682 RTOG patients

International Nuclear Information System (INIS)

Werner-Wasik, Maria; Scott, Charles; Graham, Mary L.; Smith, Colum; Byhardt, Roger W.; Roach, Mack; Andras, E. James

1997-01-01

OBJECTIVE: Radiobiologic considerations led to the choice of a 4-6 hr as an optimal interfraction interval (IFI) in hyperfractionated radiation therapy (HFX RT). Recently it was suggested (Jeremic, '95) that a shorter IFI (4.5-5.0 hr vs. 5.5-6.0) was associated with an improved survival in patients (pts) with locally advanced/inoperable non-small cell lung cancer (LA-NSCLC) treated with a concurrent chemotherapy (CT)-HFX RT or HFX RT alone. Our analysis was therefore undertaken to verify this hypothesis in a larger patient population. METHODS: Records of patients treated with HFX RT with/without CT on 5 RTOG studies were reviewed retrospectively and an actual IFI, defined as a mean of all daily IFIs, was calculated. RT dose was 1.2 Gy BID to 69.6 Gy. CT included cisplatin and either oral etoposide or vinblastine. The relationship between the length of IFI and the median survival time (MST), overall survival (OS) and incidence of esophagitis was investigated using log rank and Cox analyses. RESULTS: Pts with a LA-NSCLC were treated in 2 HFX RT only studies (n=927) and in 3 CT-HFX RT studies (n=209). Pt characteristics was as follows: Stage IIIA, 52%; Stage IIIB, 37%; males, 72%; older than 60 yr, 64%; Karnofsky Performance Status (KPS) of > 70, 84%; weight loss of >5%, 31% of pts. In 682 pts eligible for this analysis, a full dose of RT (69.6 Gy +/- 10%) was delivered and at least 90% of all daily IFIs were available. Six percent of all pts (n=42) are alive. The HFX RT studies recommended an IFI of 4-6 hr and CT-HFX RT studies, an IFI of at least 6 hr. The actual mean IFI was as follows: 4-4.9 hr in 51% of pts; 5-5.9 hr in 17%; 6-6.9 in 28% and 7-8 hr in 4%. MST and incidence of esophagitis by mean IFI are as follows: In multivariate analysis, however, only no weight loss, use of CT, low nodal stage and good KPS, but not IFI (4-6 hr vs. 6-8 hr) were associated with an improved survival for all pts (p values: <0.0001; <0.0001; 0.02; 0.0001 and 0.55, respectively), as

Effect of donor ethnicity on kidney survival in different recipient pairs: an analysis of the OPTN/UNOS database.

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Callender, C O; Cherikh, W S; Traverso, P; Hernandez, A; Oyetunji, T; Chang, D

2009-12-01

Previous multivariate analysis performed between April 1, 1994, and December 31, 2000 from the Organ Procurement Transplant Network/United Network for Organ Sharing (OPTN/UNOS) database has shown that kidneys from black donors were associated with lower graft survival. We compared graft and patient survival of different kidney donor-to-recipient ethnic combinations to see if this result still holds on a recent cohort of US kidney transplants. We included 72,495 recipients of deceased and living donor kidney alone transplants from 2001 to 2005. A multivariate Cox regression method was used to analyze the effect of donor-recipient ethnicity on graft and patient survival within 5 years of transplant, and to adjust for the effect of other donor, recipient, and transplant characteristics. Results are presented as hazard ratios (HR) with the 95% confidence limit (CL) and P values. Adjusted HRs of donor-recipient patient survival were: white to white (1); and white to black (1.22; P = .001). Graft survival HRs were black to black (1.40; P recipients. The graft and patient survival rates for Asian and Latino/Hispanic recipients, however, were not affected by donor ethnicity. This analysis underscores the need for research to better understand the reasons for these disparities and how to improve the posttransplant graft survival rates of black kidney recipients.

A MULTIVARIATE WEIBULL DISTRIBUTION

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Cheng Lee

2010-07-01

Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

Science.gov (United States)

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Treatment algorithm based on the multivariate survival analyses in patients with advanced hepatocellular carcinoma treated with trans-arterial chemoembolization.

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Hasmukh J Prajapati

Full Text Available To develop the treatment algorithm from multivariate survival analyses (MVA in patients with Barcelona clinic liver cancer (BCLC C (advanced Hepatocellular carcinoma (HCC patients treated with Trans-arterial Chemoembolization (TACE.Consecutive unresectable and non-tranplantable patients with advanced HCC, who received DEB TACE were studied. A total of 238 patients (mean age, 62.4yrs was included in the study. Survivals were analyzed according to different parameters from the time of the 1st DEB TACE. Kaplan Meier and Cox Proportional Hazard model were used for survival analysis. The SS was constructed from MVA and named BCLC C HCC Prognostic (BCHP staging system (SS.Overall median survival (OS was 16.2 months. In HCC patients with venous thrombosis (VT of large vein [main portal vein (PV, right or left PV, hepatic vein, inferior vena cava] (22.7% versus small vein (segmental/subsegmental PV (9.7% versus no VT had OSs of 6.4 months versus 20 months versus 22.8 months respectively (p<0.001. On MVA, the significant independent prognostic factors (PFs of survival were CP class, eastern cooperative oncology group (ECOG performance status (PS, single HCC<5 cm, site of VT, metastases, serum creatinine and serum alpha-feto protein. Based on these PFs, the BCHP staging system was constructed. The OSs of stages I, II and III were 28.4 months, 11.8 months and 2.4 months accordingly (p<0.001. The treatment plan was proposed according to the different stages.On MVA of patients with advanced HCC treated with TACE, significant independent prognostic factors (PFs of survival were CP class, ECOG PS, single HCC<5 cm or others, site of VT, metastases, serum creatinine and serum alpha-feto protein. New BCHP SS was proposed based on MVA data to identify the suitable advanced HCC patients for TACE treatments.

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

Applied survival analysis using R

CERN Document Server

Moore, Dirk F

2016-01-01

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

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Directory of Open Access Journals (Sweden)

Jing Zhang

2017-12-01

Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Gender, Race, and Survival: A Study in Non-Small-Cell Lung Cancer Brain Metastases Patients Utilizing the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification

International Nuclear Information System (INIS)

Videtic, Gregory M.M.; Reddy, Chandana A.; Chao, Samuel T.; Rice, Thomas W.; Adelstein, David J.; Barnett, Gene H.; Mekhail, Tarek M.; Vogelbaum, Michael A.; Suh, John H.

2009-01-01

Purpose: To explore whether gender and race influence survival in non-small-cell lung cancer (NSCLC) in patients with brain metastases, using our large single-institution brain tumor database and the Radiation Therapy Oncology Group recursive partitioning analysis (RPA) brain metastases classification. Methods and materials: A retrospective review of a single-institution brain metastasis database for the interval January 1982 to September 2004 yielded 835 NSCLC patients with brain metastases for analysis. Patient subsets based on combinations of gender, race, and RPA class were then analyzed for survival differences. Results: Median follow-up was 5.4 months (range, 0-122.9 months). There were 485 male patients (M) (58.4%) and 346 female patients (F) (41.6%). Of the 828 evaluable patients (99%), 143 (17%) were black/African American (B) and 685 (83%) were white/Caucasian (W). Median survival time (MST) from time of brain metastasis diagnosis for all patients was 5.8 months. Median survival time by gender (F vs. M) and race (W vs. B) was 6.3 months vs. 5.5 months (p = 0.013) and 6.0 months vs. 5.2 months (p = 0.08), respectively. For patients stratified by RPA class, gender, and race, MST significantly favored BFs over BMs in Class II: 11.2 months vs. 4.6 months (p = 0.021). On multivariable analysis, significant variables were gender (p = 0.041, relative risk [RR] 0.83) and RPA class (p < 0.0001, RR 0.28 for I vs. III; p < 0.0001, RR 0.51 for II vs. III) but not race. Conclusions: Gender significantly influences NSCLC brain metastasis survival. Race trended to significance in overall survival but was not significant on multivariable analysis. Multivariable analysis identified gender and RPA classification as significant variables with respect to survival.

Survival analysis of dialysis patients in selected hospitals of lahore city

International Nuclear Information System (INIS)

Ahmad, Z.; Shahzad, I.

2015-01-01

There are several reasons which are directly or indirectly relate to affect the survival time of End Stage Renal Disease (ESRD) patients. This study was done to analyse the survival rate of ESRD patients in Lahore city, and to evaluate the influence of various risk factors and prognostic factors on survival of these patients. Methods: A sample of 40 patients was taken from the Jinnah Hospital Lahore and Lahore General Hospital by using the convenience sampling technique. The Log Rank Test was used to determine the significant difference between the categories of qualitative variables of ESRD patients. Multivariate Cox Regression Analysis was used to analyse the effect of different clinical and socio-economic variables on the survival time of these patients. Results: Different qualitative variables like: age, marital status, BMI, comorbid factors, diabetes type, gender, income level, place, risk factor like diabetes, ischemic heart disease, hypertension and Hepatitis status were analysed on the basis of Log Rank Test. While age and comorbid factors were found to be statistically significant which showed that the distribution of age and comorbid factors were different. By using the Cox Regression analysis the coefficient of Mass, serum albumin and family history of diabetes were found to be significant. Conclusions: There were some of the factors which had been taken for the analysis came out less or more significant in patients of ESRD. So it was concluded that mostly clinical factors which were Mass of the Patient, Serum Albumin and Family History of Diabetes made significant contribution towards the survival status of patients. (author)

EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

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2016-12-01

Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

Multivariate Analysis of Schools and Educational Policy.

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Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

Survival benefit of radiotherapy to patients with small cell esophagus carcinoma: an analysis of Surveillance Epidemiology and End Results (SEER) data.

Science.gov (United States)

Song, Yaqi; Wang, Wanwei; Tao, Guangzhou; Zhu, Weiguo; Zhou, Xilei; Pan, Peng

2016-03-29

Small cell esophageal carcinoma (SCEC) is a rare malignant tumor. So far, few studies are found to research the effect of radiotherapy (RT) to it. This study is designed to explore the prognostic factors, and analyze survival benefit of RT to patients with SCEC. Patients with SCEC were more likely to be in female, older, higher disease stage than those with non-small cell esophageal carcinoma. RT was used in more than 50% SCEC patients. RT tended be reduced as the disease stage raise in SCEC. Univariate and multivariate analysis showed that age, year, disease stage, and RT were the prognostic factors of survival (P 0.05) and nearly 30% risks of death in distant stage (P > 0.05). SCEC patients between 1973 and 2012 were searched from the Surveillance Epidemiology and End Results (SEER) data. Clinical factors including age, year, sex, race, stage, surgery, and RT were summarized. Univariate and multivariate analysis were performed to explore the independent prognostic factors of SCEC. Cox regression survival analysis was performed to evaluate the effect of RT to SCEC based on different stages. Stage, age, year, and RT are independent prognostic factors of SCEC. Survival benefit of RT exists in any disease stage, but is only statistically significant in localized stage of SCEC.

Prognostic factorsin inoperable adenocarcinoma of the lung: A multivariate regression analysis of 259 patiens

DEFF Research Database (Denmark)

Sørensen, Jens Benn; Badsberg, Jens Henrik; Olsen, Jens

1989-01-01

The prognostic factors for survival in advanced adenocarcinoma of the lung were investigated in a consecutive series of 259 patients treated with chemotherapy. Twenty-eight pretreatment variables were investigated by use of Cox's multivariate regression model, including histological subtypes and ...

Prognostic factors for long-term outcome after percutaneous thermal ablation for hepatocellular carcinoma: a survival analysis of 137 consecutive patients

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Xu, H.-X. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Lu, M.-D. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Xie, X.-Y. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Yin, X.-Y. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Kuang, M. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Chen, J.-W. [Department of Hepatobiliary Surgery, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Xu, Z.-F. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China); Liu, G.-J. [Department of Medical Ultrasonics, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou (China)

2005-09-01

AIM: To identify prognostic factors for long-term outcome for patients with hepatocellular carcinoma (HCC) after percutaneous microwave or radiofrequency ablation. MATERIALS AND METHODS: In total, 137 consecutive patients with HCC underwent microwave or radiofrequency ablation with curative intent; 16 possible prognostic factors were evaluated for their association with overall survival (OS) and disease-free survival (DFS) using univariate and multivariate analysis. RESULTS: The median OS and DFS were 27.0 months and 8.2 months, respectively. OS rates for all patients at 1, 2, 3, 4 and 5 years were 73.9%, 52.1%, 42.8%, 26.2% and 20.1%, respectively. DFS rates at 1, 2, 3 and 4 years were 38.1%, 21.9%, 18.8%, and 14.1%, respectively. Pretreatment serum alpha-fetoprotein (AFP) >200 ng/ml, pretreatment serum albumin {<=}35 g/dl, liver function Child's class C and incomplete ablation were found to be significant predictors for OS by univariate analysis. Using multivariate analysis, incomplete ablation was identified to be the most significant independent predictor for OS. Other independent predictors for OS were serum albumin level, serum AFP level and Child-Pugh classification. Recurrence after hepatectomy and prothrombin time >14 s were identified to be significant predictors for DFS by univariate analysis, and the former was the only independent predictor for DFS by multivariate analysis. CONCLUSION: Prognosis for patients with HCC after thermal ablation with curative intent was determined by treatment response to ablation, pretreatment serum AFP, and liver function reserve. Tumour response to treatment was the most predictive factor for long-term survival and was related to tumour size, thus careful selection of patients for ablation therapy is recommended.

Prognostic factors for long-term outcome after percutaneous thermal ablation for hepatocellular carcinoma: a survival analysis of 137 consecutive patients

International Nuclear Information System (INIS)

Xu, H.-X.; Lu, M.-D.; Xie, X.-Y.; Yin, X.-Y.; Kuang, M.; Chen, J.-W.; Xu, Z.-F.; Liu, G.-J.

2005-01-01

AIM: To identify prognostic factors for long-term outcome for patients with hepatocellular carcinoma (HCC) after percutaneous microwave or radiofrequency ablation. MATERIALS AND METHODS: In total, 137 consecutive patients with HCC underwent microwave or radiofrequency ablation with curative intent; 16 possible prognostic factors were evaluated for their association with overall survival (OS) and disease-free survival (DFS) using univariate and multivariate analysis. RESULTS: The median OS and DFS were 27.0 months and 8.2 months, respectively. OS rates for all patients at 1, 2, 3, 4 and 5 years were 73.9%, 52.1%, 42.8%, 26.2% and 20.1%, respectively. DFS rates at 1, 2, 3 and 4 years were 38.1%, 21.9%, 18.8%, and 14.1%, respectively. Pretreatment serum alpha-fetoprotein (AFP) >200 ng/ml, pretreatment serum albumin ≤35 g/dl, liver function Child's class C and incomplete ablation were found to be significant predictors for OS by univariate analysis. Using multivariate analysis, incomplete ablation was identified to be the most significant independent predictor for OS. Other independent predictors for OS were serum albumin level, serum AFP level and Child-Pugh classification. Recurrence after hepatectomy and prothrombin time >14 s were identified to be significant predictors for DFS by univariate analysis, and the former was the only independent predictor for DFS by multivariate analysis. CONCLUSION: Prognosis for patients with HCC after thermal ablation with curative intent was determined by treatment response to ablation, pretreatment serum AFP, and liver function reserve. Tumour response to treatment was the most predictive factor for long-term survival and was related to tumour size, thus careful selection of patients for ablation therapy is recommended

Factors affecting survival outcomes of patients with non-metastatic Ewing's sarcoma family tumors in the spine: a retrospective analysis of 63 patients in a single center.

Science.gov (United States)

Wan, Wei; Lou, Yan; Hu, Zhiqi; Wang, Ting; Li, Jinsong; Tang, Yu; Wu, Zhipeng; Xu, Leqin; Yang, Xinghai; Song, Dianwen; Xiao, Jianru

2017-01-01

Little information has been published in the literature regarding survival outcomes of patients with Ewing's sarcoma family tumors (ESFTs) of the spine. The purpose of this study is to explore factors that may affect the prognosis of patients with non-metastatic spinal ESFTs. A retrospective analysis of survival outcomes was performed in patients with non-metastatic spinal ESFTs. Univariate and multivariate analyses were employed to identify prognostic factors for recurrence and survival. Recurrence-free survival (RFS) and overall survival (OS) were defined as the date of surgery to the date of local relapse and death. Kaplan-Meier methods were applied to estimate RFS and OS. Log-rank test was used to analyze single factors for RFS and OS. Factors with p values ≤0.1 were subjected to multivariate analysis. A total of 63 patients with non-metastatic spinal ESFTs were included in this study. The mean follow-up period was 35.1 months (range 1-155). Postoperative recurrence was detected in 25 patients, and distant metastasis and death occurred in 22 and 36 patients respectively. The result of multivariate analysis suggested that age older than 25 years and neoadjuvant chemotherapy were favorable independent prognostic factors for RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis were favorable independent prognostic factors for OS. Age older than 25 years and neoadjuvant chemotherapy are favorable prognostic factors for both RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis are closely associated with favorable survival.

Survival Analysis and its Associated Factors of Beta Thalassemia Major in Hamadan Province

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Reza Zamani

2015-05-01

Full Text Available Background: There currently is a lack of knowledge about the long-term survival of patients with beta thalassemia (BT, particularly in regions with low incidence of the disease. The aim of the present study was to determine the survival rate of the patients with BT major and the factors associated with the survival time. Methods: This retrospective cohort study was performed in Hamadan province, located in the west of Iran. The study included patients that referred to the provincial hospitals during 16 year period from 1997 to 2013. The follow up of each subject was calculated from the date of birth to the date of death. Demographic and clinical data were extracted from patients’ medical records using a checklist. Statistical analysis included the Kaplan-Meier method to analyze survivals, log-rank to compare curves between groups, and Cox regression for multivariate prognostic analysis. Results: A total of 133 patients with BT major were enrolled, 54.9% of whom were male and 66.2% were urban. The 10-, 20- and 30-year survival rate for all patients were 98.3%, 88.4% and 80.5%, respectively. Based on hazard ratio (HR, we found that accompanied diseases (P=0.01, blood type (P=0.03 and residency status (P=0.01 were significant predictors for the survival time of patients. Conclusion: The survival rate of BT patients has improved. Future researches such as prospective designs are required for the estimation of survival rate and to find other prognostic factors, which have reliable sources of data.

Multivariate Analysis for the Processing of Signals

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Beattie J.R.

2014-01-01

Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

PIXE-quantified AXSIA: Elemental mapping by multivariate spectral analysis

International Nuclear Information System (INIS)

Doyle, B.L.; Provencio, P.P.; Kotula, P.G.; Antolak, A.J.; Ryan, C.G.; Campbell, J.L.; Barrett, K.

2006-01-01

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

Particulate characterization by PIXE multivariate spectral analysis

International Nuclear Information System (INIS)

Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.

2007-01-01

Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media

Determination of wheat quality by mass spectrometry and multivariate data analysis

DEFF Research Database (Denmark)

Gottlieb, D.M.; Schultz, J.; Petersen, M.

2002-01-01

Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means...... of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis.Proteome analysis...

Prognostic factors and survival in primary malignant astrocytomas of the spinal cord: a population-based analysis from 1973 to 2007.

Science.gov (United States)

Adams, Hadie; Avendaño, Javier; Raza, Shaan M; Gokaslan, Ziya L; Jallo, George I; Quiñones-Hinojosa, Alfredo

2012-05-20

Observational cross-sectional study. Using data from the population-based cancer registries of the Surveillance, Epidemiology and End Results (SEER) program, we analyzed demographic features, tumor and treatment characteristics, as well as survival rates in patients with primary malignant astrocytomas of the spinal cord (PMASC). PMASC is a rare neoplasm and is considered to carry the same dismal outcome as their cerebral counterparts. Our current knowledge is incomplete, and understanding the epidemiology, diagnosis, and optimal treatment still poses challenges. The SEER data from 1973 to 2007 were reviewed for pathologically confirmed primary anaplastic astrocytomas (AA) and glioblastomas of the spinal cord (C72.0). We compared the clinical features and outcomes of the cohort in uni- and multivariate fashion. Survival was calculated and compared using Kaplan-Meier curves and log-rank analysis. Our search criteria retrieved 135 patients diagnosed with PMASC. The median survival for PMASC was 13 months with 1-, 2-, and 5-year survival rates of 51.8%, 32.2%, and 18.7%. Patient diagnosed with AA had a median survival time of 17 months versus 10 months in patients diagnosed with glioblastomas. Adult patients observed markedly prolonged survival compared with the pediatric group, with a 16-month versus 9-month median survival, respectively. Multivariate analysis revealed age at diagnosis, pediatric and adult age groups, sex, tumor histology, and extent of resection as significant predictors of survival. Interestingly, outcomes did not significantly change throughout the last decades or by receiving radiotherapy. Outcome for patients diagnosed with PMASC remains poor and presents an ongoing challenge for professionals in the field of neurospinal medicine and surgery. In our analyses of AA, adult patients, males, and patients undergoing radical resections were associated with increased survival. However, incidence of these lesions is low; hence, building strong

Multivariate Analysis of Industrial Scale Fermentation Data

DEFF Research Database (Denmark)

Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

2015-01-01

Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

Multivariate spectral-analysis of movement-related EEG data

International Nuclear Information System (INIS)

Andrew, C. M.

1997-01-01

The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

Essentials of multivariate data analysis

CERN Document Server

Spencer, Neil H

2013-01-01

""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

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Lijun Wang

2013-01-01

Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Multivariate Meta-Analysis Using Individual Participant Data

Science.gov (United States)

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

CERN Document Server

Zapata Mesa, Omar Andres

2016-01-01

Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

Postmastectomy Radiation Therapy Is Associated With Improved Survival in Node-Positive Male Breast Cancer: A Population Analysis

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Abrams, Matthew J., E-mail: mabrams@tuftsmedicalcenter.org [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Koffer, Paul P. [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Wazer, David E. [Department of Radiation Oncology, Tufts University School of Medicine, Tufts Medical Center, Boston, Massachusetts (United States); Department of Radiation Oncology, The Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island (United States); Hepel, Jaroslaw T. [Department of Radiation Oncology, The Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island (United States)

2017-06-01

Purpose: Because of its rarity, there are no randomized trials investigating postmastectomy radiation therapy (PMRT) in male breast cancer. This study retrospectively examines the impact of PMRT in male breast cancer patients in the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. Methods and Materials: The SEER database 8.3.2 was queried for men ages 20+ with a diagnosis of localized or regional nonmetastatic invasive ductal/lobular carcinoma from 1998 to 2013. Included patients were treated by modified radical mastectomy (MRM), with or without adjuvant external beam radiation. Univariate and multivariate analyses evaluated predictors for PMRT use after MRM. Kaplan-Meier overall survival (OS) curves of the entire cohort and a case-matched cohort were calculated and compared by the log-rank test. Cox regression was used for multivariate survival analyses. Results: A total of 1933 patients were included in the unmatched cohort. There was no difference in 5-year OS between those who received PMRT and those who did not (78% vs 77%, respectively, P=.371); however, in the case-matched analysis, PMRT was associated with improved OS at 5 years (83% vs 54%, P<.001). On subset analysis of the unmatched cohort, PMRT was associated with improved OS in men with 1 to 3 positive nodes (5-year OS 79% vs 72% P=.05) and those with 4+ positive nodes (5-year OS 73% vs 53% P<.001). On multivariate analysis of the unmatched cohort, independent predictors for improved OS were use of PMRT: HR=0.551 (0.412-0.737) and estrogen receptor–positive disease: HR=0.577 (0.339-0.983). Predictors for a survival detriment were higher grade 3/4: HR=1.825 (1.105-3.015), larger tumor T2: HR=1.783 (1.357-2.342), T3/T4: HR=2.683 (1.809-3.978), higher N-stage: N1 HR=1.574 (1.184-2.091), N2/N3: HR=2.328 (1.684-3.218), black race: HR=1.689 (1.222-2.336), and older age 81+: HR=4.164 (1.497-11.582). Conclusions: There may be a survival benefit with the

Robust methods for multivariate data analysis A1

DEFF Research Database (Denmark)

Frosch, Stina; Von Frese, J.; Bro, Rasmus

2005-01-01

Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

Improved Survival With Radiation Therapy in High-Grade Soft Tissue Sarcomas of the Extremities: A SEER Analysis

International Nuclear Information System (INIS)

Koshy, Matthew; Rich, Shayna E.; Mohiuddin, Majid M.

2010-01-01

Purpose: The benefit of radiation therapy in extremity soft tissue sarcomas remains controversial. The purpose of this study was to determine the effect of radiation therapy on overall survival among patients with primary soft tissue sarcomas of the extremity who underwent limb-sparing surgery. Methods and Materials: A retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database that included data from January 1, 1988, to December 31, 2005. A total of 6,960 patients constituted the study population. Overall survival curves were constructed using the Kaplan-Meir method and for patients with low- and high-grade tumors. Hazard ratios were calculated based on multivariable Cox proportional hazards models. Results: Of the cohort, 47% received radiation therapy. There was no significant difference in overall survival among patients with low-grade tumors by radiation therapy. In high-grade tumors, the 3-year overall survival was 73% in patients who received radiation therapy vs. 63% for those who did not receive radiation therapy (p < 0.001). On multivariate analysis, patients with high-grade tumors who received radiation therapy had an improved overall survival (hazard ratio 0.67, 95% confidence interval 0.57-0.79). In patients receiving radiation therapy, 13.5% received it in a neoadjuvant setting. The incidence of patients receiving neoadjuvant radiation did not change significantly between 1988 and 2005. Conclusions: To our knowledge, this is the largest population-based study reported in patients undergoing limb-sparing surgery for soft tissue sarcomas of the extremities. It reports that radiation was associated with improved survival in patients with high-grade tumors.

Benign meningiomas: primary treatment selection affects survival

International Nuclear Information System (INIS)

Condra, Kellie S.; Buatti, John M.; Mendenhall, William M.; Friedman, William A.; Marcus, Robert B.; Rhoton, Albert L.

1997-01-01

Purpose: To examine the effect of primary treatment selection on outcomes for benign intracranial meningiomas at the University of Florida. Methods and Materials: For 262 patients, the impact of age, Karnofsky performance status, pathologic features, tumor size, tumor location, and treatment modality on local control and cause-specific survival was analyzed (minimum potential follow-up, 2 years; median follow-up, 8.2 years). Extent of surgery was classified by Simpson grade. Treatment groups: surgery alone (n = 229), surgery and postoperative radiotherapy (RT) (n = 21), RT alone (n = 7), radiosurgery alone (n = 5). Survival analysis: Kaplan-Meier method with univariate and multivariate analysis. Results: At 15 years, local control was 76% after total excision (TE) and 87% after subtotal excision plus RT (SE+RT), both significantly better (p = 0.0001) than after SE alone (30%). Cause-specific survival at 15 years was reduced after treatment with SE alone (51%), compared with TE (88%) or SE+RT (86%) (p = 0.0003). Recurrence after primary treatment portended decreased survival, independent of initial treatment group or salvage treatment selection (p = 0.001). Atypical pathologic features predicted reduced 15-year local control (54 vs. 71%) and cause-specific survival rates (57 vs. 86%). Multivariate analysis for cause-specific survival revealed treatment group (SE vs. others; p = 0.0001), pathologic features (atypical vs. typical; p = 0.0056), and Karnofsky performance status (≥80 vs. <80; p = 0.0153) as significant variables. Conclusion: Benign meningiomas are well managed by TE or SE+RT. SE alone is inadequate therapy and adversely affects cause-specific survival. Atypical pathologic features predict a poorer outcome, suggesting possible benefit from more aggressive treatment. Because local recurrence portends lower survival rates, primary treatment choice is important

Survival Analysis

CERN Document Server

Miller, Rupert G

2011-01-01

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

Auto-SCT improves survival in systemic light chain amyloidosis: a retrospective analysis with 14-year follow-up.

Science.gov (United States)

Parmar, S; Kongtim, P; Champlin, R; Dinh, Y; Elgharably, Y; Wang, M; Bashir, Q; Shah, J J; Shah, N; Popat, U; Giralt, S A; Orlowski, R Z; Qazilbash, M H

2014-08-01

Optimal treatment approach continues to remain a challenge for systemic light chain amyloidosis (AL). So far, Auto-SCT is the only modality associated with long-term survival. However, failure to show survival benefit in randomized study raises questions regarding its efficacy. We present a comparative outcome analysis of Auto-SCT to conventional therapies (CTR) in AL patients treated over a 14-year period at our institution. Out of the 145 AL amyloidosis patients, Auto-SCT was performed in 80 patients with 1-year non-relapse mortality rate of 12.5%. Novel agents were used as part of induction therapy in 56% of transplant recipients vs 46% of CTR patients. Hematological and organ responses were seen in 74.6% and 39% in the Auto-SCT arm vs 53% and 12% in the CTR arm, respectively. The projected 5-year survival for Auto-SCT vs CTR was 63% vs 38%, respectively. Landmark analysis of patients alive at 1-year after diagnosis showed improved 5-year OS of 72% with Auto-SCT vs 65% in the CTR arm. In the multivariate analysis, age SCT were associated with improved survival. In conclusion, Auto-SCT is associated with long-term survival for patients with AL amyloidosis.

Multivariate Generalized Multiscale Entropy Analysis

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Anne Humeau-Heurtier

2016-11-01

Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

Multivariate calibration applied to the quantitative analysis of infrared spectra

Energy Technology Data Exchange (ETDEWEB)

Haaland, D.M.

1991-01-01

Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

Directory of Open Access Journals (Sweden)

Binod Neupane

Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when

PSMA targeted radioligandtherapy in metastatic castration resistant prostate cancer after chemotherapy, abiraterone and/or enzalutamide. A retrospective analysis of overall survival

Energy Technology Data Exchange (ETDEWEB)

Rahbar, K.; Schaefers, M. [University Hospital Muenster, Department of Nuclear Medicine, Muenster (Germany); Boegemann, M. [University Hospital Muenster, Department of Urology, Muenster (Germany); Yordanova, A.; Essler, M.; Ahmadzadehfar, H. [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Eveslage, M. [University Hospital Muenster, Institute of Biostatistics and Clinical Research, Muenster (Germany)

2018-01-15

Our aim was to evaluate overall survival and parameters prognosticating longer survival in a large and homogeneous group of patients treated with {sup 177}Lu-PSMA-617 radioligand therapy with heavily pretreated advanced metastatic castration resistant prostate cancer. A total of 104 patients were treated with 351 cycles of {sup 177}Lu-PSMA-617. Prostate specific antigen (PSA) changes after the first cycle of therapy were documented prior to a second cycle. Patients were followed-up for overall survival (OS). Any PSA decline, PSA decline ≥50%, initial PSA, alkaline phosphatase (ALP), lactate dehydrogenase (LDH), visceral metastases and cumulative injected activity were analyzed and evaluated according to OS. Multivariable analysis with parameters with a p-value ≤0.05 in univariate analysis was performed, additionally adjusting for age and presence of visceral metastases. A total of 51 patients (49%) died during the observation period. The majority of patients (97%) presented with bone metastases, 77% with lymph node metastases and 32% with visceral metastases. All patients were treated with at least one line of chemotherapy. Either abiraterone or enzalutamide had been given in 100% of the patients. Any PSA decline occurred in 70 (67%) and a PSA decline ≥50% in 34 (33%) of patients after the first cycle. The median OS was 56.0 weeks (95%CI: 50.5-61.5). Initial PSA decline ≥50%, initial LDH, visceral metastases, second line chemotherapy or prior radium-223 did not have an effect on survival, whereas any initial PSA decline, initial ALP <220 U/L and cumulative injected activity ≥18.8 GBq were associated with a longer survival. A step-by-step analysis revealed a PSA decline ≥20.87% as the most noticeable cut-off prognosticating longer survival, which remained an independent prognosticator of improved OS in the multivariate analysis. {sup 177}Lu-PSMA-617 RLT is a new effective therapeutic and seems to prolong survival in patients with advanced m

Parathyroid carcinoma survival: improvements in the era of intact parathyroid hormone monitoring?

Directory of Open Access Journals (Sweden)

Steve R. Martinez

2013-02-01

Full Text Available The intact parathyroid hormone (iPTH assay is a critical test in the diagnosis and management of PTH-mediated hypercalcemia, including parathyroid carcinoma (PCa. We hypothesized that the survival of patients diagnosed with PCa has improved since adoption of the iPTH assay into clinical practice. We identified all confirmed cases of PCa within the Surveillance, Epidemiology and End Results database from 1973 to 2006. Patients were categorized into two eras based upon introduction of the iPTH assay: 1973 to 1997 (era I and 1997 to 2006 (era II, when the iPTH assay was in standard use. We estimated overall survival (OS and disease-specific survival (DSS using the Kaplan-Meier method, with differences among survival curves assessed via log rank. Multivariate Cox proportional hazards models compared the survival rates between treatment eras while controlling for patient age, sex, race/ethnicity, tumor size, nodal status, extent of disease, and type of surgery. Multivariate models included patients undergoing potentially curative surgery and excluded those with dis- tant metastases. Risks of overall and disease-specific mortality were reported as hazard ratios with 95% confidence intervals. Study criteria were met by 370 patients. Median survival was 15.6 years. Five-year rates of OS and DSS were 78% and 88% for era I and 82% and 96% for era II. On multivariate analysis, age, black race, and unknown extent of disease predicted an increased risk of death from any cause. Treatment era did not predict OS. No factor predicted PCa-specific mortality. In multivariate analysis, neither OS nor DSS have improved in the current era that utilizes iPTH for the detection and management of PCa.

Power Estimation in Multivariate Analysis of Variance

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Jean François Allaire

2007-09-01

Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

Challenging a dogma: five-year survival does not equal cure in all colorectal cancer patients.

Science.gov (United States)

Abdel-Rahman, Omar

2018-02-01

The current study tried to evaluate the factors affecting 10- to 20- years' survival among long term survivors (>5 years) of colorectal cancer (CRC). Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was queried through SEER*Stat program.Univariate probability of overall and cancer-specific survival was determined and the difference between groups was examined. Multivariate analysis for factors affecting overall and cancer-specific survival was also conducted. Among node positive patients (Dukes C), 34% of the deaths beyond 5 years can be attributed to CRC; while among M1 patients, 63% of the deaths beyond 5 years can be attributed to CRC. The following factors were predictors of better overall survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus rectal location), earlier stage and surgery (P <0.0001 for all parameters). Similarly, the following factors were predictors of better cancer-specific survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus left colon and rectal locations), earlier stage and surgery (P <0.0001 for all parameters). Among node positive long-term CRC survivors, more than one third of all deaths can be attributed to CRC.

Rituximab is associated with improved survival in Burkitt lymphoma: a retrospective analysis from two US academic medical centers.

Science.gov (United States)

Wildes, Tanya M; Farrington, Laura; Yeung, Cecilia; Harrington, Alexandra M; Foyil, Kelley V; Liu, Jingxia; Kreisel, Friederike; Bartlett, Nancy L; Fenske, Timothy S

2014-02-01

Burkitt lymphoma (BL) is a rare, highly aggressive B-cell malignancy treated most successfully with brief-duration, high-intensity chemotherapeutic regimens. The benefit of the addition of rituximab to these regimens remains uncertain. We sought to examine the effectiveness of chemotherapy with and without rituximab in patients with BL. This study is a retrospective cohort study of all adult patients with BL diagnosed and treated with modern, dose-intense chemotherapeutic regimens from 1998-2008 at two tertiary care institutions. All cases were confirmed by application of WHO 2008 criteria by hematopathologists. Medical records were reviewed for patient-, disease-, and treatment- related factors as well as treatment response and survival. Factors associated with survival were analyzed using Cox proportional hazards modeling. A total of 35 patients were analyzed: 18 patients received rituximab with chemotherapy (R-chemo) and 17 received chemotherapy (chemo) alone. The median age was 42 (range 20-74 years); 57% were male; 71% had Ann Arbor Stage IV disease; 33% had central nervous system involvement; 78% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. R-chemo was associated with significantly longer overall survival (OS) than chemo alone (5-year OS 70% and 29%, respectively, p = 0.040). On multivariate regression analysis, poor performance status and central nervous system involvement were associated with poorer survival. The addition of rituximab to chemotherapy was associated with improved OS in patients with Burkitt lymphoma. Poor performance status and central nervous system involvement were prognostically significant on multivariate analysis.

Some developments in multivariate image analysis

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey

be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...

A comparison between multivariate and bivariate analysis used in marketing research

Directory of Open Access Journals (Sweden)

Constantin, C.

2012-01-01

Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

PROGNOSTIC FACTORS FOR SURVIVAL IN PATIENTS WITH METASTATIC COLORECTAL CANCER TREATED WITH FIRST - LINE CHEMOTHERAPY

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Deyan Davidov

2017-05-01

Full Text Available Objective: The aim of this study was to investigate the prognostic significance for survival of certain clinical and pathological factors in patients with advanced or metastatic colorectal carcinoma (CRC treated with first- line chemotherapy. Methods: From 2002 to 2011 seventy- four consecutive patients with advanced or metastatic CRC, treated in UMHAT- Dr. G. Stranski, Department of Medical Oncology entered the study. Some patient’s characteristics, hematological and pathological parameters, were evaluated for their role as predictors of overall survival. The therapeutic regimens included FOLFOX or FOlFIRI. Survival analysis was evaluated by Kaplan- Meier test. The influence of pretreatment characteristics as prognostic factor for survival was analyzed using multivariate stepwise Cox regression analyses. Results: In multivariate analysis a significant correlation was exhibited between survival, poor performance status and multiple sites of metastasis. Variables significantly associated with overall survival in univariate analysis were performance status>1, thrombocytosis, anemia and number of metastatic sites >1. Conclusion: These results indicated that poor performance status, anemia, thrombocytosis as well as multiple site of metastasis could be useful prognostic factors in patients with metastatic CRC.

Multivariate Volatility Impulse Response Analysis of GFC News Events

NARCIS (Netherlands)

D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)

2015-01-01

markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the

Multivariant design and multiple criteria analysis of building refurbishments

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Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

2005-07-01

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

Data classification and MTBF prediction with a multivariate analysis approach

International Nuclear Information System (INIS)

Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

2012-01-01

The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

Multivariate Volatility Impulse Response Analysis of GFC News Events

NARCIS (Netherlands)

D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

2015-01-01

textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the

Epidermal growth factor receptor: an independent predictor of survival in astrocytic tumors given definitive irradiation

International Nuclear Information System (INIS)

An Zhu; Shaeffer, James; Leslie, Susan; Kolm, Paul; El-Mahdi, Anas M.

1996-01-01

Purpose: To determine whether the expression of epidermal growth factor receptor (EGFR) protein was predictive of patient survival independently of other prognostic factors in astrocytic tumors. Methods and Materials: Epidermal growth factor receptor protein expression was investigated immunohistochemically in formalin-fixed, paraffin-embedded surgical specimens of 55 glioblastoma multiforme, 14 anaplastic astrocytoma, and 2 astrocytomas given definitive irradiation. We evaluated the relationship of EGFR protein expression and tumor grade, histologic features, age at diagnosis, sex, patient survival, and recurrence-free survival. Results: The percentage of tumor cells which were EGFR positive related to reduced survival by Cox regression analysis in both univariate (p = 0.0424) and multivariate analysis (p = 0.0016). Epidermal growth factor receptor positivity was the only 1 of 11 clinical and histological variables associated with decreased recurrence-free survival by either univariate (p = 0.0353) or multivariate (p = 0.0182) analysis. Epidermal growth factor receptor protein expression was not related to patient age, sex, or histologic features. Conclusion: Epidermal growth factor receptor positivity was a significant and independent prognostic indicator for overall survival and recurrence-free survival for irradiated patients with astrocytic gliomas

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

Science.gov (United States)

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Neutrophil-to-lymphocyte ratio as an independent predictor for survival in patients with localized clear cell renal cell carcinoma after radiofrequency ablation: a propensity score matching analysis.

Science.gov (United States)

Chang, Xiaofeng; Zhang, Fan; Liu, Tieshi; Wang, Wei; Guo, Hongqian

2017-06-01

To investigate the role of neutrophil-to-lymphocyte ratio as a prognostic indicator in patients with localized clear cell renal cell carcinoma treated with radiofrequency ablation. We retrospectively analyzed data from patients with renal cell carcinoma who underwent radiofrequency ablation from 2006 to 2013. The Kaplan-Meier method was used to generate the survival curves according to different categories of neutrophil-to-lymphocyte ratio. Relationships between preoperative neutrophil-to-lymphocyte ratio or the change of neutrophil-to-lymphocyte ratio and survival were evaluated with multivariable Cox proportional hazards regression analysis. A propensity score matching analysis was carried out to avoid confounding bias. A total of 185 patients were included in present study. When stratified by preoperative neutrophil-to-lymphocyte ratio cutoff value of 2.79, 5-year recurrence-free survival, 5-year disease-free survival, and 5-year overall survival rates of neutrophil-to-lymphocyte ratio analysis, 5-year recurrence-free survival, 5-year disease-free survival, and 5-year overall survival rates of neutrophil-to-lymphocyte ratio ratio with the change of neutrophil-to-lymphocyte ratio, patients with both preoperative neutrophil-to-lymphocyte ratio ≥2.79 and the change of neutrophil-to-lymphocyte ratio ≥0.40 had the worst disease-free survival. Results of multivariable analysis showed that preoperative neutrophil-to-lymphocyte ratio and the change of neutrophil-to-lymphocyte ratio correlated with cancer relapse remarkably. High preoperative neutrophil-to-lymphocyte ratio and elevated postoperative neutrophil-to-lymphocyte ratio are associated with significant increase in risk of local recurrence as well as distant metastasis. The combination of neutrophil-to-lymphocyte ratio with the other prognostic indicators can be applied in the evaluation of relapse risk in patients with clear cell renal cell carcinoma after radiofrequency ablation.

Multivariate data analysis

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg

Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

Progesterone receptor levels independently predict survival in endometrial adenocarcinoma

DEFF Research Database (Denmark)

Nyholm, H C; Christensen, Ib Jarle; Nielsen, Anette Lynge

1995-01-01

to correlations to cancer-specific survival in a multivariate analysis including histopathological characteristics. Median patient follow-up time was 67 months with 18 cancer deaths. The PR-DCC and ER-DCC values were dichotomized according to levels previously found by us to correspond to the best agreement...... between receptor status as determined by the DCC and ICA methods (130 fmol/mg cytosol protein for ER, 114 fmol/mg for PR). Using these thresholds, we found by multivariate analysis that "high" PR-DCC levels (> 114 fmol/mg) correlated significantly (P = 0.004) with survival, independent of stage risk group...... could not be statistically evaluated due to the number of cases with eligible ICA values. However, we suggest that owing to a close correlation between DCC and ICA results, PR-ICA status may provide significant prognostic information when DCC measurements are not available....

Pregnancy associated nasopharyngeal carcinoma: A retrospective case-control analysis of maternal survival outcomes

International Nuclear Information System (INIS)

Cheng, Yi-Kan; Zhang, Fan; Tang, Ling-Long; Chen, Lei; Zhou, Guan-Qun; Zeng, Mu-Sheng; Kang, Tie-Bang; Jia, Wei-Hua; Shao, Jian-Yong; Mai, Hai-Qiang; Guo, Ying; Ma, Jun

2015-01-01

Background: Pregnancy-associated nasopharyngeal carcinoma (PANPC) has been associated with poor survival. Recent advances in radiation technology and imaging techniques, and the introduction of chemotherapy have improved survival in nasopharyngeal carcinoma (NPC); however, it is not clear whether these changes have improved survival in PANPC. Therefore, the purpose of this study was to compare five-year maternal survival in patients with PANPC and non-pregnant patients with NPC. Methods: After adjusting for age, stage and chemotherapy mode, we conducted a retrospective case-control study among 36 non-metastatic PANPC patients and 36 non-pregnant NPC patients (control group) who were treated at our institution between 2000 and 2010. Results: The median age of both groups was 30 years (range, 23–35 years); median follow-up for all patients was 70 months. Locoregionally-advanced disease accounted for 83.3% of all patients with PANPC and 92.9% of patients who developed NPC during pregnancy. In both the PANPC and control groups, 31 patients (86.1%) received chemotherapy and all patients received definitive radiotherapy. The five-year rates for overall survival (70% vs. 78%, p = 0.72), distant metastasis-free survival (79% vs. 76%, p = 0.77), loco-regional relapse-free survival (97% vs. 91%, p = 0.69) and disease-free survival (69% vs. 74%, p = 0.98) were not significantly different between the PANPC and control groups. Multivariate analysis using a Cox proportional hazards model revealed that only N-classification was significantly associated with five-year OS. Conclusion: This study demonstrates that, in the modern treatment era, pregnancy itself may not negatively influence survival outcomes in patients with NPC; however, pregnancy may delay the diagnosis of NPC

Survival of a cohort of women with cervical cancer diagnosed in a Brazilian cancer center

Directory of Open Access Journals (Sweden)

Claudio Calazan do Carmo

2011-08-01

Full Text Available OBJECTIVE: To assess overall survival of women with cervical cancer and describe prognostic factors associated. METHODS: A total of 3,341 cases of invasive cervical cancer diagnosed at the Brazilian Cancer Institute, Rio de Janeiro, southeastern Brazil, between 1999 and 2004 were selected. Clinical and pathological characteristics and follow-up data were collected. There were performed a survival analysis using Kaplan-Meier curves and a multivariate analysis through Cox model. RESULTS: Of all cases analyzed, 68.3% had locally advanced disease at the time of diagnosis. The 5-year overall survival was 48%. After multivariate analysis, tumor staging at diagnosis was the single variable significantly associated with prognosis (p<0.001. There was seen a dose-response relationship between mortality and clinical staging, ranging from 27.8 to 749.6 per 1,000 cases-year in women stage I and IV, respectively. CONCLUSIONS: The study showed that early detection through prevention programs is crucial to increase cervical cancer survival.

Multivariate Analysis and Prediction of Dioxin-Furan ...

Science.gov (United States)

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

On the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model

International Nuclear Information System (INIS)

Unkel, Steffen; Belka, Claus; Lauber, Kirsten

2016-01-01

the extraction of scores of radioresistance, which displayed significant correlations with the estimated parameters of the regression models. Undoubtedly, LQ regression is a robust method for the analysis of clonogenic survival data. Nevertheless, alternative approaches including non-linear regression and multivariate techniques such as cluster analysis and principal component analysis represent versatile tools for the extraction of parameters and/or scores of the cellular response towards ionizing irradiation with a more intuitive biological interpretation. The latter are highly informative for correlation analyses with other types of data, including functional genomics data that are increasingly beinggenerated

Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

International Nuclear Information System (INIS)

Garcia-Cabrejo, Oscar; Valocchi, Albert

2014-01-01

Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data

DEFF Research Database (Denmark)

Buch-Kromann, Tine; Nielsen, Jens

2012-01-01

This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing prior...

Search for the top quark using multivariate analysis techniques

International Nuclear Information System (INIS)

Bhat, P.C.

1994-08-01

The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the eμ channel and neural networks to the e+jets channel

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

Science.gov (United States)

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Lung Shunt Fraction prior to Yttrium-90 Radioembolization Predicts Survival in Patients with Neuroendocrine Liver Metastases: Single-Center Prospective Analysis

Energy Technology Data Exchange (ETDEWEB)

Ludwig, Johannes M. [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States); Ambinder, Emily McIntosh [John Hopkins University School of Medicine, Department of Diagnostic Radiology (United States); Ghodadra, Anish [University of Pittsburgh School of Medicine, Interventional Radiology, Department of Radiology (United States); Xing, Minzhi [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States); Prajapati, Hasmukh J. [The University of Tennessee Health Science Center, Division of Interventional Radiology, Department of Radiology (United States); Kim, Hyun S., E-mail: kevin.kim@yale.edu [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States)

2016-07-15

ObjectiveTo investigate survival outcomes following radioembolization with Yttrium-90 (Y90) for neuroendocrine tumor liver metastases (NETLMs). This study was designed to assess the efficacy of Y90 radioembolization and to evaluate lung shunt fraction (LSF) as a predictor for survival.MethodsA single-center, prospective study of 44 consecutive patients (median age: 58.5 years, 29.5 % male) diagnosed with pancreatic (52.3 %) or carcinoid (47.7 %) NETLMs from 2006 to 2012 who underwent Y90 radioembolization was performed. Patients’ baseline characteristics, including LSF and median overall survival (OS) from first Y90 radioembolization, were recorded and compared between patients with high (≥10 %) and low (<10 %) LSF. Baseline comparisons were performed using Fisher’s exact tests for categorical and Mann–Whitney U test for continuous variables. Survival was calculated using the Kaplan–Meier method. Univariate (Wilcoxon rank-sum test) and multivariate analyses (Cox Proportional Hazard Model) for risk factor analysis were performed.ResultsThere was no statistically significant difference in age, gender, race, tumor properties, or previous treatments between patients with high (n = 15) and low (n = 29) LSF. The median OS was 27.4 months (95 %CI 12.73–55.23), with 4.77 months (95 %CI 2.87–26.73) for high and 42.77 months (95 %CI 18.47–59.73) for low LSF (p = 0.003). Multivariate analysis identified high LSF (p = 0.001), total serum bilirubin >1.2 mg (p = 0.016), and lack of pretreatment with octreotide (p = 0.01) as independent prognostic factors for poorer survival. Tumor type and total radiation dose did not predict survival.ConclusionsLSF ≥10 %, elevated bilirubin levels, and lack of pretreatment with octreotide were found to be independent prognostic factors for poorer survival in patients with NETLMs.

Clinical Predictors of Survival for Patients with Stage IV Cancer Referred to Radiation Oncology.

Directory of Open Access Journals (Sweden)

Johnny Kao

Full Text Available There is an urgent need for a robust, clinically useful predictive model for survival in a heterogeneous group of patients with metastatic cancer referred to radiation oncology.From May 2012 to August 2013, 143 consecutive patients with stage IV cancer were prospectively evaluated by a single radiation oncologist. We retrospectively analyzed the effect of 29 patient, laboratory and tumor-related prognostic factors on overall survival using univariate analysis. Variables that were statistically significant on univariate analysis were entered into a multivariable Cox regression to identify independent predictors of overall survival.The median overall survival was 5.5 months. Four prognostic factors significantly predicted survival on multivariable analysis including ECOG performance status (0-1 vs. 2 vs. 3-4, number of active tumors (1 to 5 vs. ≥ 6, albumin levels (≥ 3.4 vs. 2.4 to 3.3 vs. 31.4 months for very low risk patients compared to 14.5 months for low risk, 4.1 months for intermediate risk and 1.2 months for high risk (p < 0.001.These data suggest that a model that considers performance status, extent of disease, primary tumor site and serum albumin represents a simple model to accurately predict survival for patients with stage IV cancer who are potential candidates for radiation therapy.

Comparison of colorectal and gastric cancer: Survival and prognostic factors

International Nuclear Information System (INIS)

Moghimi-Dehkordi, Bijan; Safaee, Azadeh; Zali, Mohammad R

2009-01-01

Gastric and colorectal cancers are the most common gastrointestinal malignancies in Iran. We aim to compare the survival rates and prognostic factors between these two cancers. We studied 1873 patients with either gastric or colorectal cancer who were registered in one referral cancer registry center in Tehran, Iran. All patients were followed from their time of diagnosis until December 2006 (as failure time). Survival curves were calculated according to the Kaplan-Meier Method and compared by the Log-rank test. Multivariate analysis of prognostic factors was carried out using the Cox proportional hazard model. Of 1873 patients, there were 746 with gastric cancer and 1138 with colorectal cancer. According to the Kaplan-Meier method 1, 3, 5, and 7-year survival rates were 71.2, 37.8, 25.3, and 19.5%, respectively, in gastric cancer patients and 91.1, 73.1, 61, and 54.9%, respectively, in patients with colorectal cancer. Also, univariate analysis showed that age at diagnosis, sex, grade of tumor, and distant metastasis were of prognostic significance in both cancers ( P < 0.0001). However, in multivariate analysis, only distant metastasis in colorectal cancer and age at diagnosis, grade of tumor, and distant metastasis in colorectal cancer were identified as independent prognostic factors influencing survival. According to our findings, survival is significantly related to histological differentiation of tumor and distant metastasis in colorectal cancer patients and only to distant metastasis in gastric cancer patients. (author)

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1996-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

Multivariate techniques of analysis for ToF-E recoil spectrometry data

Energy Technology Data Exchange (ETDEWEB)

Whitlow, H J; Bouanani, M E; Persson, L; Hult, M; Jonsson, P; Johnston, P N [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M; Zaring, C [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P N; Bubb, I F; Walker, B R; Stannard, W B [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D D; Dytlewski, N [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1997-12-31

Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

The value of external beam radiation in pathologic node positive prostate cancer: a multivariate analysis

International Nuclear Information System (INIS)

Morris, Astrid D.; Zietman, Anthony L.; Althausen, Alex F.; Heney, Niall M.; Kaufman, Donald S.; Shipley, William U.

1997-01-01

Purpose: The goal of this study was to evaluate the effect of local/regional treatment, particularly external beam radiation alone versus radical prostatectomy and radiation therapy in patients with pathologic node positive prostate cancer on survival. The effect of delayed vs. immediate endocrine therapy on patients treated with radiation alone was also examined. Methods: Medical records of all 116 patients who received their initial treatment at the Massachusetts General Hospital between 1980 and 1996 for adenocarcinoma of the prostate with pathologic confirmed nodal metastasis and no distant disease were reviewed. The mean follow up was 5.5 years. Disease specific survival, time to PSA failure on endocrine therapy, and time to first intervention were evaluated. PSA failure was defined as two consecutive post-nadir rises following the first use of endocrine therapy. Intervention was defined as any surgical or radiotherapeutic procedure required for relief of symptoms related to local/regional recurrence. Survival comparisons were made between any local/regional treatment vs. none, radiation therapy alone vs. prostatectomy with radiation therapy, and immediate vs. delayed endocrine therapy. The effect of the different treatment options on survival were compared using multivariate Cox proportional hazard models to simultaneously adjust for patient and tumor characteristics (tumor stage, Gleason grade, number of positive nodes) that might influence survival. Results: The combined patient population had a 5 year disease specific survival of 74% and a 10 year disease specific survival of 48%. The comparison groups for local/regional treatment had the following adjusted outcomes. In a subgroup analysis of patients with clinical T1-T2 and clinical T3-T4 disease, local/regional treatment continued to confer a disease specific survival advantage over no local regional treatment in both subgroups (p=0.05 and p=0.02, respectively). PSA failure on endocrine therapy was

Advanced event reweighting using multivariate analysis

International Nuclear Information System (INIS)

Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J

2012-01-01

Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.

Five-year survival in 309 patients with colorectal liver metastases treated with radiofrequency ablation

International Nuclear Information System (INIS)

Gillams, A.R.; Lees, W.R.

2009-01-01

There is little published long-term survival data for patients with colorectal liver metastases treated with radiofrequency ablation (RFA). We present a multivariate analysis of 5-year survival in 309 patients (198 male, aged 64 (24-92)) treated at 617 sessions. Our standard protocol used internally cooled electrodes introduced percutaneously under combined US and CT guidance/monitoring. The number and size of liver metastases, the presence and location of extrahepatic disease, primary resection, clinical, chemotherapy and follow-up data were recorded. Data analysis was performed using SPSS v.10. On multivariate analysis, significant survival factors were the presence of extrahepatic disease (p < 0.001) and liver tumour volume (p = 0.001). For 123 patients with five or less metastases of 5 cm or less maximum diameter and no extrahepatic disease median survival was 46 and 36 months from liver metastasis diagnosis and ablation, respectively; corresponding 3- and 5-year survival rates were 63%, 34% and 49%, 24%. Sixty-nine patients had three or less tumours of below 3.5 cm in diameter and their 5-year survival from ablation was 33%. There were 23/617(3.7%) local complications requiring intervention. Five-year survival of 24-33% post ablation in selected patients is superior to any published chemotherapy data and approaches the results of liver resection. (orig.)

Natural history definition and a suggested clinical approach to Buerger's disease: a case-control study with survival analysis.

Science.gov (United States)

Fazeli, Bahare; Ravari, Hassan; Assadi, Reza

2012-08-01

The aim of this study was first to describe the natural history of Buerger's disease (BD) and then to discuss a clinical approach to this disease based on multivariate analysis. One hundred eight patients who corresponded with Shionoya's criteria were selected from 2000 to 2007 for this study. Major amputation was considered the ultimate adverse event. Survival analyses were performed by Kaplan-Meier curves. Independent variables including gender, duration of smoking, number of cigarettes smoked per day, minor amputation events and type of treatments, were determined by multivariate Cox regression analysis. The recorded data demonstrated that BD may present in four forms, including relapsing-remitting (75%), secondary progressive (4.6%), primary progressive (14.2%) and benign BD (6.2%). Most of the amputations occurred due to relapses within the six years after diagnosis of BD. In multivariate analysis, duration of smoking of more than 20 years had a significant relationship with further major amputation among patients with BD. Smoking cessation programs with experienced psychotherapists are strongly recommended for those areas in which Buerger's disease is common. Patients who have smoked for more than 20 years should be encouraged to quit smoking, but should also be recommended for more advanced treatment for limb salvage.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

Science.gov (United States)

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Men and women show similar survival outcome in stage IV breast cancer.

Science.gov (United States)

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Point defect characterization in HAADF-STEM images using multivariate statistical analysis

International Nuclear Information System (INIS)

Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

2011-01-01

Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

Determinants of survival after liver resection for metastatic colorectal carcinoma.

Science.gov (United States)

Parau, Angela; Todor, Nicolae; Vlad, Liviu

2015-01-01

Prognostic factors for survival after liver resection for metastatic colorectal cancer identified up to date are quite inconsistent with a great inter-study variability. In this study we aimed to identify predictors of outcome in our patient population. A series of 70 consecutive patients from the oncological hepatobiliary database, who had undergone curative hepatic surgical resection for hepatic metastases of colorectal origin, operated between 2006 and 2011, were identified. At 44.6 months (range 13.7-73), 30 of 70 patients (42.85%) were alive. Patient demographics, primary tumor and liver tumor factors, operative factors, pathologic findings, recurrence patterns, disease-free survival (DFS), overall survival (OS) and cancer-specific survival (CSS) were analyzed. Clinicopathologic variables were tested using univariate and multivariate analyses. The 3-year CSS after first hepatic resection was 54%. Median CSS survival after first hepatic resection was 40.2 months. Median CSS after second hepatic resection was 24.2 months. The 3-year DFS after first hepatic resection was 14%. Median disease free survival after first hepatic resection was 18 months. The 3-year DFS after second hepatic resection was 27% and median DFS after second hepatic resection 12 months. The 30-day mortality and morbidity rate after first hepatic resection was 5.71% and 12.78%, respectively. In univariate analysis CSS was significantly reduced for the following factors: age >53 years, advanced T stage of primary tumor, moderately- poorly differentiated tumor, positive and narrow resection margin, preoperative CEA level >30 ng/ml, DFS <18 months. Perioperative chemotherapy related to metastasectomy showed a trend in improving CSS (p=0.07). Perioperative chemotherapy improved DFS in a statistically significant way (p=0.03). Perioperative chemotherapy and achievement of resection margins beyond 1 mm were the major determinants of both CSS and DFS after first liver resection in multivariate

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

Science.gov (United States)

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide

CERN Document Server

Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P

2007-01-01

Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.

Atrial fibrillation and survival in colorectal cancer

Directory of Open Access Journals (Sweden)

Justin Timothy A

2004-11-01

Full Text Available Abstract Background Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer. Patients and methods A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively. Results A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test. However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival. Conclusion The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis.

Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

DEFF Research Database (Denmark)

Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

Metal concentration at surface water using multivariate analysis and ...

African Journals Online (AJOL)

Metal concentration at surface water using multivariate analysis and human health risk assessment. F Azaman, H Juahir, K Yunus, A Azid, S.I. Khalit, A.D. Mustafa, M.A. Amran, C.N.C. Hasnam, M.Z.A.Z. Abidin, M.A.M. Yusri ...

Prognostic nutritional index is associated with survival after total gastrectomy for patients with gastric cancer.

Science.gov (United States)

Ishizuka, Mitsuru; Oyama, Yusuke; Abe, Akihito; Tago, Kazuma; Tanaka, Genki; Kubota, Keiichi

2014-08-01

To investigate the influence of clinical characteristics including nutritional markers on postoperative survival in patients undergoing total gastrectomy (TG) for gastric cancer (GC). One hundred fifty-four patients were enrolled. Uni- and multivariate analyses using the Cox proportional hazard model were performed to explore the most valuable clinical characteristic that was associated with postoperative survival. Multivariate analysis using twelve clinical characteristics selected from univariate analyses revealed that age (≤ 72/>72), carcinoembryonic antigen (≤ 20/>20) (ng/ml), white blood cell count (≤ 9.5/>9.5) (× 10(3)/mm(3)), prognostic nutritional index (PNI) (≤ 45/>45) and lymph node metastasis (negative/positive) were associated with postoperative survival. Kaplan-Meier analysis and log-rank test showed that patients with higher PNI (>45) had a higher postoperative survival rate than those with lower PNI (≤ 45) (p<0.001). PNI is associated with postoperative survival of patients undergoing TG for GC and is able to divide such patients into two independent groups before surgery. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Multivariate statistical analysis of atom probe tomography data

International Nuclear Information System (INIS)

Parish, Chad M.; Miller, Michael K.

2010-01-01

The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.

The Impact of Chemoembolization Endpoints on Survival in Hepatocellular Carcinoma Patients

Science.gov (United States)

Jin, Brian; Wang, Dingxin; Lewandowski, Robert J.; Riaz, Ahsun; Ryu, Robert K.; Sato, Kent T.; Larson, Andrew C.; Salem, Riad; Omary, Reed A.

2010-01-01

OBJECTIVE To investigate the relationship between angiographic embolic endpoints of transarterial chemoembolization (TACE) and survival in patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS This study retrospectively assessed 105 patients with surgically unresectable HCC who underwent TACE. Patients were classified according to a previously established subjective angiographic chemoembolization endpoint (SACE) scale. Only one patient was classified as SACE level 1 and thus excluded from all subsequent analysis. Survival was evaluated with Kaplan-Meier analysis. Multivariate analysis with Cox’s proportional hazard regression model was used to determine independent prognostic risk factors of survival. RESULTS Overall median survival was 21.1 months (95% confidence interval [CI], 15.9–26.4). Patients embolized to SACE levels 2 and 3 were aggregated and had a significantly higher median survival (25.6 months; 95% CI, 16.2–35.0) than patients embolized to SACE level 4 (17.1 months; 95% CI, 13.3–20.9) (p = 0.035). Multivariate analysis indicated that SACE level 4 (Hazard ratio [HR], 2.49; 95% CI, 1.41–4.42; p = 0.002), European Cooperative Oncology Group performance status > 0 (HR, 1.97; 95% CI, 1.15–3.37; p = 0.013), American Joint Committee on Cancer stage 3 or 4 (HR, 2.42; 95% CI, 1.27–4.60; p = 0.007), and Child-Pugh class B (HR, 1.94; 95% CI, 1.09–3.46; p = 0.025) were all independent negative prognostic indicators of survival. CONCLUSION Embolization to an intermediate, sub-stasis endpoint (SACE levels 2 and 3) during TACE improves survival compared to embolization to a higher, stasis endpoint (SACE level 4). Interventional oncologists should consider targeting these intermediate, sub-stasis angiographic endpoints during TACE. PMID:21427346

Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

KAUST Repository

Malvaso, Federica

2015-05-05

Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

Treatment and survival outcomes of small cell carcinoma of the esophagus: an analysis of the National Cancer Data Base.

Science.gov (United States)

Wong, Andrew T; Shao, Meng; Rineer, Justin; Osborn, Virginia; Schwartz, David; Schreiber, David

2017-02-01

Given the paucity of esophageal small cell carcinoma (SCC) cases, there are few large studies evaluating this disease. In this study, the National Cancer Data Base (NCDB) was utilized to analyze the clinical features, treatment, and survival of patients with esophageal SCC in a large, population-based dataset. We selected patients diagnosed with esophageal SCC from 1998 to 2011. Patients were identified as having no treatment, chemotherapy alone, radiation ± sequential chemotherapy, concurrent chemoradiation, and esophagectomy ± chemotherapy and/or radiation. Overall survival (OS) was analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariate Cox regression analysis was conducted to identify factors associated with OS. A total of 583 patients were identified. Most patients had stage IV disease (41.7%). Regarding treatment selection, chemoradiation was the most commonly utilized for patients with nonmetasatic disease, whereas chemotherapy alone was most common for metastatic patients. Esophagectomy (median survival 44.9 months with 3 year OS 50.5%) was associated with the best OS for patients with localized (node-negative) disease compared with chemotherapy alone (p < 0.001) or chemoradiation (p = 0.01). For locoregional (node-positive) disease, treatment with chemoradiation resulted in a median survival of 17.8 months and a 3 year OS 31.6%. On multivariate analysis, treatment with chemotherapy alone (p = 0.003) was associated with worse OS while esophagectomy (p = 0.04) was associated with improved OS compared to chemoradiation. Esophageal SCC is an aggressive malignancy with most patients presenting with metastatic disease. Either esophagectomy or chemoradiation as part of multimodality treatment appear to improve OS for selected patients with nonmetastatic disease. © 2016 International Society for Diseases of the Esophagus.

Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

International Nuclear Information System (INIS)

Martin, Madhavi Z.; Labbe, Nicole; Rials, Timothy G.; Wullschleger, Stan D.

2005-01-01

In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate

Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

Energy Technology Data Exchange (ETDEWEB)

Martin, Madhavi Z. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States); Labbe, Nicole [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States)]. E-mail: nlabbe@utk.edu; Rials, Timothy G. [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States); Wullschleger, Stan D. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States)

2005-08-31

In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate.

Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

Czech Academy of Sciences Publication Activity Database

Novák, J.; Sůvová, H.; Vondráček, Jiří

2002-01-01

Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

The prognostic factors affecting survival in muscle invasive bladder cancer treated with radiotherapy

International Nuclear Information System (INIS)

Chung, Woong Ki; Oh, Bong Ryoul; Ahn, Sung Ja; Nah, Byung Sik; Kwon, Dong Deuk; Park, Kwang Sung; Ryu, Soo Bang; Park, Yang Il

2002-01-01

This study analyzed the prognostic factors affecting the survival rate and evaluated the role of radiation therapy in muscle-invading bladder cancer. Twenty eight patient with bladder cancer who completed planned definitive radiotherapy in the Departments of Therapeutic Radiology and Urology, Chonnam National University Hospital between Jan. 1986 to Dec. 1998 were retrospectively analyzed. The reviews were performed based on the patients' medical records. There were 21 males and 7 females in this study. The median of age was 72 years old ranging from 49 to 84 years. All patients were confirmed as having transitional cell carcinoma with histological grade 1 in one patient, grade 2 in 15, grade 3 in 9, and uniformed in 3. Radiation therapy was performed using a linear accelerator with 6 or 10 MV X-rays. Radiation was delivered daily with a 1.8 or 2.0 Gy fraction size by 4 ports (anterior-posterior, both lateral, alternatively) or 3 ports (Anterior and both lateral). The median radiation dose delivered to the isocenter of the target volume was 61.24 Gy ranging from 59 to 66.6 Gy. The survival rate was calculated by the Kaplan-Meier method. Multivariate analysis was performed on the prognostic factors affecting the survival rate. The survival rate was 76%, 46%, 33%, 33% at 1, 2, 3, 5 years, respectively, with 19 months of median survival. The potential factors of age (less than 70 years vs above 70), sex, diabetes mellitus, hypertension, hydronephrosis, T-stage (T3a vs T3b), TUR, chemotherapy, total duration of radiotherapy, radiation dose (less than 60 Gy vs above 60 Gy), and the treatment response were investigated with uni- and multivariate analysis. In univariate analysis, the T-stage (ρ 0.078) and radiation dose (ρ = 0.051) were marginally significant, and the treatment response (ρ = 0.011) was a statistically significant factor on the survival rate. Multivariate analysis showed there were no significant prognostic factors affecting the survival rate. The

The prognostic factors affecting survival in muscle invasive bladder cancer treated with radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Chung, Woong Ki; Oh, Bong Ryoul; Ahn, Sung Ja; Nah, Byung Sik; Kwon, Dong Deuk; Park, Kwang Sung; Ryu, Soo Bang; Park, Yang Il [Chonnam National University Medical School, Chonnam National University Hospital, Kwangju (Korea, Republic of)

2002-06-15

This study analyzed the prognostic factors affecting the survival rate and evaluated the role of radiation therapy in muscle-invading bladder cancer. Twenty eight patient with bladder cancer who completed planned definitive radiotherapy in the Departments of Therapeutic Radiology and Urology, Chonnam National University Hospital between Jan. 1986 to Dec. 1998 were retrospectively analyzed. The reviews were performed based on the patients' medical records. There were 21 males and 7 females in this study. The median of age was 72 years old ranging from 49 to 84 years. All patients were confirmed as having transitional cell carcinoma with histological grade 1 in one patient, grade 2 in 15, grade 3 in 9, and uniformed in 3. Radiation therapy was performed using a linear accelerator with 6 or 10 MV X-rays. Radiation was delivered daily with a 1.8 or 2.0 Gy fraction size by 4 ports (anterior-posterior, both lateral, alternatively) or 3 ports (Anterior and both lateral). The median radiation dose delivered to the isocenter of the target volume was 61.24 Gy ranging from 59 to 66.6 Gy. The survival rate was calculated by the Kaplan-Meier method. Multivariate analysis was performed on the prognostic factors affecting the survival rate. The survival rate was 76%, 46%, 33%, 33% at 1, 2, 3, 5 years, respectively, with 19 months of median survival. The potential factors of age (less than 70 years vs above 70), sex, diabetes mellitus, hypertension, hydronephrosis, T-stage (T3a vs T3b), TUR, chemotherapy, total duration of radiotherapy, radiation dose (less than 60 Gy vs above 60 Gy), and the treatment response were investigated with uni- and multivariate analysis. In univariate analysis, the T-stage ({rho} 0.078) and radiation dose ({rho} = 0.051) were marginally significant, and the treatment response ({rho} = 0.011) was a statistically significant factor on the survival rate. Multivariate analysis showed there were no significant prognostic factors affecting the survival

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

Science.gov (United States)

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

International Nuclear Information System (INIS)

Fendler, Wolfgang Peter; Ilhan, Harun; Paprottka, Philipp M.; Jakobs, Tobias F.; Heinemann, Volker; Bartenstein, Peter; Haug, Alexander R.; Khalaf, Feras; Ezziddin, Samer; Hacker, Marcus

2015-01-01

Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

Energy Technology Data Exchange (ETDEWEB)

Fendler, Wolfgang Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Klinik und Poliklinik fuer Nuklearmedizin, Munich (Germany); Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Paprottka, Philipp M. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Jakobs, Tobias F. [Hospital Barmherzige Brueder, Department of Diagnostic and Interventional Radiology, Munich (Germany); Heinemann, Volker [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter; Haug, Alexander R. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Khalaf, Feras [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Ezziddin, Samer [Saarland University Medical Center, Department of Nuclear Medicine, Homburg (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

2015-09-15

Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Science.gov (United States)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

International Nuclear Information System (INIS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-01-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

Energy Technology Data Exchange (ETDEWEB)

Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

2004-06-15

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

Metformin Use and Endometrial Cancer Survival

Science.gov (United States)

Nevadunsky, Nicole S.; Van Arsdale, Anne; Strickler, Howard D.; Moadel, Alyson; Kaur, Gurpreet; Frimer, Marina; Conroy, Erin; Goldberg, Gary L.; Einstein, Mark H.

2013-01-01

Objective Impaired glucose tolerance and diabetes are risk factors for the development of uterine cancer. Although greater progression free survival among diabetic patients with ovarian and breast cancer using metformin have been reported, no studies have assessed the association of metformin use with survival in women with endometrial cancer (EC). Methods We conducted a single-institution retrospective cohort study of all patients treated for uterine cancer from January 1999 through December 2009. Demographic, medical, social, and survival data were abstracted from medical records and the national death registry. Overall survival (OS) was estimated using Kaplan-Meier methods. Cox models were utilized for multivariate analysis. All statistical tests were two-sided. Results Of 985 patients, 114 (12%) had diabetes and were treated with metformin, 136 (14%) were diabetic but did not use metformin, and 735 (74%) had not been diagnosed with diabetes. Greater OS was observed in diabetics with non-endometrioid EC who used metformin than in diabetic cases not using metformin and non-endometrioid EC cases without diabetes (log rank test (p=0.02)). This association remained significant (hazard ratio = 0.54, 95% CI: 0.30–0.97, p<0.04) after adjusting for age, clinical stage, grade, chemotherapy treatment, radiation treatment and presence of hyperlipidemia in multivariate analysis. No association between metformin use and OS in diabetics with endometrioid histology was observed. Conclusion Diabetic EC patients with non-endometrioid tumors who used metformin had lower risk of death than women with EC who did not use metformin. These data suggest that metformin might be useful as adjuvant therapy for non-endometrioid EC. PMID:24189334

Survival chance in papillary thyroid cancer in Hungary: individual survival probability estimation using the Markov method

International Nuclear Information System (INIS)

Esik, Olga; Tusnady, Gabor; Daubner, Kornel; Nemeth, Gyoergy; Fuezy, Marton; Szentirmay, Zoltan

1997-01-01

Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients with a median follow-up time of 7.1 years to establish a clinical database for uni- and multivariate analysis of the prognostic factors related to survival (Kaplan-Meier product limit method and Cox regression). For a more precise prognosis estimation, the effect of the most important clinical events were then investigated on the basis of a Markov renewal model. The basic concept of this approach is that each patient has an individual disease course which (besides the initial clinical categories) is affected by special events, e.g. internal covariates (local/regional/distant relapses). On the supposition that these events and the cause-specific death are influenced by the same biological processes, the parameters of transient survival probability characterizing the speed of the course of the disease for each clinical event and their sequence were determined. The individual survival curves for each patient were calculated by using these parameters and the independent significant clinical variables selected from multivariate studies, summation of which resulted in a mean cause-specific survival function valid for the entire group. On the basis of this Markov model, prediction of the cause-specific survival probability is possible for extrastudy cases, if it is supposed that the clinical events occur within new patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor size and extent (pT), the external irradiation dosage and the degree of TSH suppression proved to be

Multivariate statistics exercises and solutions

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

Retrospective Analysis of the Survival Benefit of Induction Chemotherapy in Stage IVa-b Nasopharyngeal Carcinoma.

Science.gov (United States)

Lan, Xiao-Wen; Zou, Xue-Bin; Xiao, Yao; Tang, Jie; OuYang, Pu-Yun; Su, Zhen; Xie, Fang-Yun

2016-01-01

The value of adding induction chemotherapy to chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) remains controversial, yet high-risk patients with LA-NPC have poor outcomes after chemoradiotherapy. We aimed to assess the survival benefits of induction chemotherapy in stage IVa-b NPC. A total of 602 patients with stage IVa-b NPC treated with intensity-modulated radiation therapy (IMRT) and concurrent chemotherapy with or without induction chemotherapy were retrospectively analyzed. Overall survival (OS), locoregional relapse-free survival (LRFS), distant metastasis-free survival (DMFS) and progression-free survival (PFS) were evaluated using the Kaplan-Meier method, log-rank test and Cox regression analysis. In univariate analysis, 5-year OS was 83.2% for induction chemotherapy plus concurrent chemotherapy and 74.8% for concurrent chemotherapy alone, corresponding to an absolute risk reduction of 8.4% (P = 0.022). Compared to concurrent chemotherapy alone, addition of induction chemotherapy improved 5-year DMFS (83.2% vs. 74.4%, P = 0.018) but not 5-year LRFS (83.7% vs. 83.0%, P = 0.848) or PFS (71.9% vs. 66.0%, P = 0.12). Age, T category, N category, chemotherapy strategy and clinical stage were associated with 5-year OS (P = 0.017, P = 0.031, P = 0.007, P = 0.022, P = 0.001, respectively). In multivariate analysis, induction chemotherapy plus concurrent chemotherapy was an independent favorable prognostic factor for OS (HR, 0.62; 95% CI, 0.43-0.90, P = 0.012) and DMFS (HR, 0.57; 95% CI, 0.38-0.83, P = 0.004). In subgroup analysis, induction chemotherapy significantly improved 5-year DMFS in stage IVa (86.8% vs. 77.3%, P = 0.008), but provided no significant benefit in stage IVb. In patients with stage IVa-b NPC treated with IMRT, addition of induction chemotherapy to concurrent chemotherapy significantly improved 5-year OS and 5-year DMFS. This study provides a basis for selection of high risk patients in future clinical therapeutic

Biochemical Control With Radiotherapy Improves Overall Survival in Intermediate and High-Risk Prostate Cancer Patients Who Have an Estimated 10-Year Overall Survival of >90%

International Nuclear Information System (INIS)

Herbert, Christopher; Liu, Mitchell; Tyldesley, Scott; Morris, W. James; Joffres, Michel; Khaira, Mandip; Kwan, Winkle; Moiseenko, Vitali; Pickles, Thomas

2012-01-01

Purpose: To identify subgroups of patients with carcinoma of the prostate treated with radical radiotherapy that have improved overall survival when disease is biochemically controlled. Methods and Materials: A cohort of 1,060 prostate cancer patients treated with radical radiotherapy was divided into nine subgroups based on National Comprehensive Cancer Network risk category and estimated 10-year overall survival (eOS 10y) derived from the age adjusted Charlson Comorbidity Index. Patients with and without biochemical control were compared with respect to overall survival. Actuarial estimates of overall survival were calculated using the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards models were used for analysis of overall survival. Results: Median follow-up was 125 months (range, 51–176 months). Only the subgroups with high or intermediate risk disease and an eOS 10y of >90% had a statistically significantly improved overall survival when prostate cancer was biochemically controlled. In all other groups, biochemical control made no significant difference to overall survival. In the subgroup with high-risk disease and eOS 10y >90%, actuarial overall survival was 86.3% (95% confidence interval [CI] 78.5%–94.1%) and 62.1% (95% CI 52.9%–71.3%) for patients with biochemical control and biochemical relapse respectively (p = 0.002). In the intermediate risk group with eOS >90%, actuarial overall survival was 95.3% (95% CI 89.0%–100%) and 79.8% (95% CI 68.0%–91.6%) for biochemically controlled and biochemically relapsed patients (p = 0.033). On multivariate analysis, National Comprehensive Cancer Network risk group (p = 0.005), biochemical control (p = 0.033) and eOS 10y (p 90%.

Effecst of Patho- Biological Factors on the Survival of Recurrent Breast Cancer Cases

Science.gov (United States)

Akbari, Mohammad Esmaeil; Rohani- Rasaf, Marzieh; Nafissi, Nahid; Akbari, Atieh; Shojaee, Leyla

2018-04-25

Background: Recurrence of breast cancer after treatment is generally due to loco-regional invasion or distant metastasis. Although patients with metastasis are considered incurable, existing treatments might prolong a patient’s life while also improving its quality. Choice of approach for individual patients requires identification of relevant survival factors. This study concerns factors influencing survival after recurrence in Iranian breast cancer patients. Methods: This study was performed on 442 recurrent breast cancer patients referred to the Cancer Research Center of Shahid Beheshti University between 1985 and 2015. After confirming recurrence as a distant metastasis or loco-regional invasion, the effects of demographic, clinic-pathologic, biological, type of surgery and type of adjuvant treatment on survival were evaluated using univariate and multivariate stratified Cox models. Results: The mean survival after recurrence was 18 months (5 days to 13 years), 219 patients (70.42%) survived two years, 75 patients (24.12%) survived from 2 to 5 years, and 17 patients (5.47%) survived more than 5 years. In this study, it was found through univariate analysis that the factors of age, lymph node status, DFI, place of recurrence and nodal ratio demonstrated greatest influence on survival after recurrence. On multivariate analysis, the most important factors influencing survival were the place of recurrence and the lymph node status. Conclusion: The results of this study enhance our knowledge of effects of different factors on survival of patients after breast cancer recurrence. Thus, they may be used to inform treatment choice. Creative Commons Attribution License

Study of ionically modified water performance in carbonate reservoir system by multivariate data analysis

DEFF Research Database (Denmark)

Sohal, Muhammad Adeel Nassar; Kucheryavskiy, Sergey V.; Thyne, Geoffrey

2017-01-01

the critical mechanisms at the pore scale. Better pore scale physico-chemical understanding will guide to formulate accurate reservoir-scale models. This paper presents a comprehensive meta-analysis of the proposed mechanisms using multivariate data analysis. Detailed review of the subject, including...... mechanisms with supporting and contradictory evidence has been presented by Sohal et al. (2016). In this study, the significance of each contributing factor to EOR was quantified and subjected to rigorous multivariate statistical analysis. The analysis was limited because there is no uniform methodology...

High serum uric acid concentration predicts poor survival in patients with breast cancer.

Science.gov (United States)

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

The studies of post-medieval glass by multivariate and X-ray fluorescence analysis

International Nuclear Information System (INIS)

Kierzek, J.; Kunicki-Goldfinger, J.

2002-01-01

Multivariate statistical analysis of the results obtained by energy dispersive X-ray fluorescence analysis has been used in the study of baroque vessel glasses originated from central Europe. X-ray spectrometry can be applied as a completely non-destructive, non-sampling and multi-element method. It is very useful in the studies of valuable historical artefacts. For the last years, multivariate statistical analysis has been developed as an important tool for the archaeometric purposes. Cluster, principal component and discriminant analysis were applied for the classification of the examined objects. The obtained results show that these statistical tools are very useful and complementary in the studies of historical objects. (author)

Multivariable analysis: a practical guide for clinicians and public health researchers

National Research Council Canada - National Science Library

Katz, Mitchell H

2011-01-01

.... It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae...

Survival Outcomes in Resected Extrahepatic Cholangiocarcinoma: Effect of Adjuvant Radiotherapy in a Surveillance, Epidemiology, and End Results Analysis

International Nuclear Information System (INIS)

Vern-Gross, Tamara Z.; Shivnani, Anand T.; Chen, Ke; Lee, Christopher M.; Tward, Jonathan D.; MacDonald, O. Kenneth; Crane, Christopher H.; Talamonti, Mark S.; Munoz, Louis L.; Small, William

2011-01-01

Purpose: The benefit of adjuvant radiotherapy (RT) after surgical resection for extrahepatic cholangiocarcinoma has not been clearly established. We analyzed survival outcomes of patients with resected extrahepatic cholangiocarcinoma and examined the effect of adjuvant RT. Methods and Materials: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program between 1973 and 2003. The primary endpoint was the overall survival time. Cox regression analysis was used to perform univariate and multivariate analyses of the following clinical variables: age, year of diagnosis, histologic grade, localized (Stage T1-T2) vs. regional (Stage T3 or greater and/or node positive) stage, gender, race, and the use of adjuvant RT after surgical resection. Results: The records for 2,332 patients were obtained. Patients with previous malignancy, distant disease, incomplete or conflicting records, atypical histologic features, and those treated with preoperative/intraoperative RT were excluded. Of the remaining 1,491 patients eligible for analysis, 473 (32%) had undergone adjuvant RT. After a median follow-up of 27 months (among surviving patients), the median overall survival time for the entire cohort was 20 months. Patients with localized and regional disease had a median survival time of 33 and 18 months, respectively (p < .001). The addition of adjuvant RT was not associated with an improvement in overall or cause-specific survival for patients with local or regional disease. Conclusion: Patients with localized disease had significantly better overall survival than those with regional disease. Adjuvant RT was not associated with an improvement in long-term overall survival in patients with resected extrahepatic bile duct cancer. Key data, including margin status and the use of combined chemotherapy, was not available through the SEER database.

The prognostic role of mTOR and p-mTOR for survival in non-small cell lung cancer: a systematic review and meta-analysis.

Directory of Open Access Journals (Sweden)

Lei Li

Full Text Available The mammalian target of rapamycin (mTOR and phosphorylated mTOR (p-mTOR are potential prognostic markers and therapeutic targets for non-small cell lung cancer (NSCLC. However, the association between mTOR/p-mTOR expression and NSCLC patients' prognosis remains controversial. Thus, a meta-analysis of existing studies evaluating the prognostic role of mTOR/p-mTOR expression for NSCLC was conducted.A systemically literature search was performed via Pubmed, Embase, Medline as well as CNKI (China National Knowledge Infrastructure. Studies were included that reported the hazard ratio (HR and 95%CI for the association between mTOR/p-mTOR expression and NSCLC patients' survival. Random-effects model was used to pool HRs.Ten eligible studies were included in this meta-analysis, with 4 about m-TOR and 7 about p-mTOR. For mTOR, the pooled HR of overall survival (OS was 1.00 (95%CI 0.5 to 1.99 by univariate analysis and 1.22 (95%CI 0.53 to 2.82 by multivariate analysis. For p-mTOR, the pooled HR was 1.39 (95%CI 0.97 to 1.98 by univariate analysis and 1.42 (95%CI 0.56 to 3.60 by multivariate analysis.The results indicated that no statistically significant association was found between mTOR/p-mTOR expression and NSCLC patients' prognosis.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

Science.gov (United States)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Use of multivariate extensions of generalized linear models in the analysis of data from clinical trials

OpenAIRE

ALONSO ABAD, Ariel; Rodriguez, O.; TIBALDI, Fabian; CORTINAS ABRAHANTES, Jose

2002-01-01

In medical studies the categorical endpoints are quite often. Even though nowadays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed. multivariate analysis; multivariate logistic regression; multicategor...

Outcome of cardiopulmonary resuscitation - predictors of survival

International Nuclear Information System (INIS)

Ishtiaq, O.; Iqbal, M.; Zubair, M.; Qayyum, R.; Adil, M.

2008-01-01

To assess the outcomes of patients undergoing cardiopulmonary resuscitation (CPR). Data were collected retrospectively of all adult patients who underwent CPR. Clinical outcomes of interest were survival at the end of CPR and survival at discharge from hospital. Factors associated with survival were evaluated using logistic regression analysis. Of the 159 patients included, 55 (35%) were alive at the end of CPR and 17 (11%) were discharged alive from the hospital. At the end of CPR, univariate logistic regression analysis found the following factors associated with survival: cardiac arrest within hospital as compared to outside the hospital (odds ratio = 2.8, 95% CI = 1.27-6.20, p-value = 0.01), both cardiac and pulmonary arrest as compared to either cardiac or pulmonary arrest (odds ratio = 0.37, 95% CI = 0.19- 0.73, p-value = 0.004), asystole as cardiac rhythm at presentation (odds ratio = 0.47, 95% CI = 0.24-0.93, p-value = 0.03), and total atropine dose given during CPR (odds ratio = 0.78, 95% CI = 0.62-0.97, p-value = 0.02). In multivariate logistic regression, cardiac arrest within hospital (odds ratio = 2.52, 95% CI = 1.06-5.99, p-value = 0.04) and both cardiac and pulmonary arrest as compared to cardiac or pulmonary arrest (odds ratio = 0.44, 95% CI = 0.21-0.91, p-value = 0.03) were associated with survival at the end of CPR. At the time of discharge from hospital, univariate logistic regression analysis found following factors that were associated with survival: cardiac arrest within hospital (odds ratio = 8.4, 95% CI = 1.09-65.64, p-value = 0.04), duration of CPR (odds ratio = 0.91, 95% CI = 0.85-0.96, p-value = 0.001), and total atropine dose given during CPR (odds ratio = 0.68, 95% CI = 0.47-0.99, p-value = 0.05). In multivariate logistic regression analysis cardiac arrest within hospital (odds ratio 8.69, 95% CI = 1.01-74.6, p-value = 0.05) and duration of CPR (odds ratio 0.92, 95% CI = 0.87-0.98, p-value = 0.01) were associated with survival at

Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

Science.gov (United States)

Lin, Deborah S; Taylor, Peter; Tibbett, Mark

2014-10-01

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Serum CA125 predicts extrauterine disease and survival in uterine carcinosarcoma

Science.gov (United States)

Huang, Gloria S.; Chiu, Lydia G.; Gebb, Juliana S.; Gunter, Marc J.; Sukumvanich, Paniti; Goldberg, Gary L.; Einstein, Mark H.

2009-01-01

Objective The purpose of this study was to determine the clinical utility of CA125 measurement in patients with uterine carcinosarcoma (CS). Methods Ninety-five consecutive patients treated for CS at a single institution were identified. All 54 patients who underwent preoperative CA125 measurement were included in the study. Data were abstracted from the medical records. Tests of association between preoperative CA125 and previously identified clinicopathologic prognostic factors were performed using Fisher’s exact test and Pearson chi-square test. To evaluate relationship of CA125 elevation and survival, a Cox proportional hazard model was used for multivariate analysis, incorporating all of prognostic factors identified by univariate analysis. Results Preoperative CA125 was significantly associated with the presence of extrauterine disease (P<0.001), deep myometrial invasion (P<0.001), and serous histology of the epithelial component (P=0.005). Using univariate survival analysis, stage (HR=1.808, P=0.004), postoperative CA125 level (HR=9.855, P<0.001), and estrogen receptor positivity (HR=0.314, P=0.029) were significantly associated with survival. In the multivariate model, only postoperative CA125 level remained significantly associated with poor survival (HR=5.725, P=0.009). Conclusion Preoperative CA125 elevation is a marker of extrauterine disease and deep myometrial invasion in patients with uterine CS. Postoperative CA125 elevation is an independent prognostic factor for poor survival. These findings indicate that CA125 may be a clinically useful serum marker in the management of patients with CS. PMID:17935762

Understanding survival analysis: Kaplan-Meier estimate.

Science.gov (United States)

Goel, Manish Kumar; Khanna, Pardeep; Kishore, Jugal

2010-10-01

Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. We label these situations as censored observations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. The survival curve can be created assuming various situations. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.

Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

International Nuclear Information System (INIS)

Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T.

1993-01-01

149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs

Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

Energy Technology Data Exchange (ETDEWEB)

Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

1993-07-01

149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

Classification of adulterated honeys by multivariate analysis.

Science.gov (United States)

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

Science.gov (United States)

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

Embolotherapy for Neuroendocrine Tumor Liver Metastases: Prognostic Factors for Hepatic Progression-Free Survival and Overall Survival

International Nuclear Information System (INIS)

Chen, James X.; Rose, Steven; White, Sarah B.; El-Haddad, Ghassan; Fidelman, Nicholas; Yarmohammadi, Hooman; Hwang, Winifred; Sze, Daniel Y.; Kothary, Nishita; Stashek, Kristen; Wileyto, E. Paul; Salem, Riad; Metz, David C.; Soulen, Michael C.

2017-01-01

PurposeThe purpose of the study was to evaluate prognostic factors for survival outcomes following embolotherapy for neuroendocrine tumor (NET) liver metastases.Materials and MethodsThis was a multicenter retrospective study of 155 patients (60 years mean age, 57 % male) with NET liver metastases from pancreas (n = 71), gut (n = 68), lung (n = 8), or other/unknown (n = 8) primary sites treated with conventional transarterial chemoembolization (TACE, n = 50), transarterial radioembolization (TARE, n = 64), or transarterial embolization (TAE, n = 41) between 2004 and 2015. Patient-, tumor-, and treatment-related factors were evaluated for prognostic effect on hepatic progression-free survival (HPFS) and overall survival (OS) using unadjusted and propensity score-weighted univariate and multivariate Cox proportional hazards models.ResultsMedian HPFS and OS were 18.5 and 125.1 months for G1 (n = 75), 12.2 and 33.9 months for G2 (n = 60), and 4.9 and 9.3 months for G3 tumors (n = 20), respectively (p  50 % hepatic volume demonstrated 5.5- and 26.8-month shorter median HPFS and OS, respectively, versus burden ≤50 % (p < 0.05). There were no significant differences in HPFS or OS between gut or pancreas primaries. In multivariate HPFS analysis, there were no significant differences among embolotherapy modalities. In multivariate OS analysis, TARE had a higher hazard ratio than TACE (unadjusted Cox model: HR 2.1, p = 0.02; propensity score adjusted model: HR 1.8, p = 0.11), while TAE did not differ significantly from TACE.ConclusionHigher tumor grade and tumor burden prognosticated shorter HPFS and OS. TARE had a higher hazard ratio for OS than TACE. There were no significant differences in HPFS among embolotherapy modalities.

Embolotherapy for Neuroendocrine Tumor Liver Metastases: Prognostic Factors for Hepatic Progression-Free Survival and Overall Survival

Energy Technology Data Exchange (ETDEWEB)

Chen, James X. [Hospital of the University of Pennsylvania, Division of Interventional Radiology, Department of Radiology (United States); Rose, Steven [University of San Diego Medical Center, Division of Interventional Radiology, Department of Radiology (United States); White, Sarah B. [Medical College of Wisconsin, Division of Interventional Radiology, Department of Radiology (United States); El-Haddad, Ghassan [Moffitt Cancer Center, Division of Interventional Radiology, Department of Radiology (United States); Fidelman, Nicholas [University of San Francisco Medical Center, Division of Interventional Radiology, Department of Radiology (United States); Yarmohammadi, Hooman [Memorial Sloan Kettering Cancer Center, Division of Interventional Radiology, Department of Radiology (United States); Hwang, Winifred; Sze, Daniel Y.; Kothary, Nishita [Stanford University Medical Center, Division of Interventional Radiology, Department of Radiology (United States); Stashek, Kristen [Hospital of the University of Pennsylvania, Department of Pathology (United States); Wileyto, E. Paul [University of Pennsylvania, Department of Biostatistics and Epidemiology (United States); Salem, Riad [Northwestern Memorial Hospital, Division of Interventional Radiology, Department of Radiology (United States); Metz, David C. [Hospital of the University of Pennsylvania, Division of Gastroenterology, Department of Medicine (United States); Soulen, Michael C., E-mail: michael.soulen@uphs.upenn.edu [Hospital of the University of Pennsylvania, Division of Interventional Radiology, Department of Radiology (United States)

2017-01-15

PurposeThe purpose of the study was to evaluate prognostic factors for survival outcomes following embolotherapy for neuroendocrine tumor (NET) liver metastases.Materials and MethodsThis was a multicenter retrospective study of 155 patients (60 years mean age, 57 % male) with NET liver metastases from pancreas (n = 71), gut (n = 68), lung (n = 8), or other/unknown (n = 8) primary sites treated with conventional transarterial chemoembolization (TACE, n = 50), transarterial radioembolization (TARE, n = 64), or transarterial embolization (TAE, n = 41) between 2004 and 2015. Patient-, tumor-, and treatment-related factors were evaluated for prognostic effect on hepatic progression-free survival (HPFS) and overall survival (OS) using unadjusted and propensity score-weighted univariate and multivariate Cox proportional hazards models.ResultsMedian HPFS and OS were 18.5 and 125.1 months for G1 (n = 75), 12.2 and 33.9 months for G2 (n = 60), and 4.9 and 9.3 months for G3 tumors (n = 20), respectively (p < 0.05). Tumor burden >50 % hepatic volume demonstrated 5.5- and 26.8-month shorter median HPFS and OS, respectively, versus burden ≤50 % (p < 0.05). There were no significant differences in HPFS or OS between gut or pancreas primaries. In multivariate HPFS analysis, there were no significant differences among embolotherapy modalities. In multivariate OS analysis, TARE had a higher hazard ratio than TACE (unadjusted Cox model: HR 2.1, p = 0.02; propensity score adjusted model: HR 1.8, p = 0.11), while TAE did not differ significantly from TACE.ConclusionHigher tumor grade and tumor burden prognosticated shorter HPFS and OS. TARE had a higher hazard ratio for OS than TACE. There were no significant differences in HPFS among embolotherapy modalities.

3D Quantitative tumour burden analysis in patients with hepatocellular carcinoma before TACE: comparing single-lesion vs. multi-lesion imaging biomarkers as predictors of patient survival

International Nuclear Information System (INIS)

Fleckenstein, Florian N.; Schernthaner, Ruediger E.; Duran, Rafael; Sohn, Jae Ho; Sahu, Sonia; Zhao, Yan; Hamm, Bernd; Gebauer, Bernhard; Lin, MingDe; Geschwind, Jean-Francois; Chapiro, Julius

2016-01-01

To compare the ability of single- vs. multi-lesion assessment on baseline MRI using 1D- and 3D-based measurements to predict overall survival (OS) in patients with hepatocellular carcinoma (HCC) before transarterial chemoembolization (TACE). This retrospective analysis included 122 patients. A quantitative 3D analysis was performed on baseline MRI to calculate enhancing tumour volume (ETV [cm 3 ]) and enhancing tumour burden (ETB [%]) (ratio between ETV [cm 3 ] and liver volume). Furthermore, enhancing and overall tumour diameters were measured. Patients were stratified into two groups using thresholds derived from the BCLC staging system. Statistical analysis included Kaplan-Meier plots, uni- and multivariate cox proportional hazard ratios (HR) and concordances. All methods achieved good separation of the survival curves (p < 0.05). Multivariate analysis showed an HR of 5.2 (95 % CI 3.1-8.8, p < 0.001) for ETV [cm 3 ] and HR 6.6 (95 % CI 3.7-11.5, p < 0.001) for ETB [%] vs. HR 2.6 (95 % CI 1.2-5.6, p = 0.012) for overall diameter and HR 3.0 (95 % CI 1.5-6.3, p = 0.003) for enhancing diameter. Concordances were highest for ETB [%], with no added predictive power for multi-lesion assessment (difference between concordances not significant). 3D quantitative assessment is a stronger predictor of survival as compared to diameter-based measurements. Assessing multiple lesions provides no substantial improvement in predicting OS than evaluating the dominant lesion alone. (orig.)

Pattern recognition by the use of multivariate statistical evaluation of macro- and micro-PIXE results

International Nuclear Information System (INIS)

Tapper, U.A.S.; Malmqvist, K.G.; Loevestam, N.E.G.; Swietlicki, E.; Salford, L.G.

1991-01-01

The importance of statistical evaluation of multielemental data is illustrated using the data collected in a macro- and micro-PIXE analysis of human brain tumours. By employing a multivariate statistical classification methodology (SIMCA) it was shown that the total information collected from each specimen separates three types of tissue: High malignant, less malignant and normal brain tissue. This makes a classification of a given specimen possible based on the elemental concentrations. Partial least squares regression (PLS), a multivariate regression method, made it possible to study the relative importance of the examined nine trace elements, the dry/wet weight ratio and the age of the patient in predicting the survival time after operation for patients with the high malignant form, astrocytomas grade III-IV. The elemental maps from a microprobe analysis were also subjected to multivariate analysis. This showed that the six elements sorted into maps could be presented in three maps containing all the relevant information. The intensity in these maps is proportional to the value (score) of the actual pixel along the calculated principal components. (orig.)

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

Energy Technology Data Exchange (ETDEWEB)

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

2012-10-15

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

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

International Nuclear Information System (INIS)

Malagari, Katerina; Pomoni, Mary; Moschouris, Hippocrates; Bouma, Evanthia; Koskinas, John; Stefaniotou, Aspasia; Marinis, Athanasios; Kelekis, Alexios; Alexopoulou, Efthymia; Chatziioannou, Achilles; Chatzimichael, Katerina; Dourakis, Spyridon; Kelekis, Nikolaos; Rizos, Spyros; Kelekis, Dimitrios

2012-01-01

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

Utilization pattern and survival outcomes of adjuvant therapies in high-grade nonretroperitoneal abdominal soft tissue sarcoma: A population-based study.

Science.gov (United States)

Green, William Ross; Chokshi, Ravi; Jabbour, Salma K; DeLaney, Thomas F; Mahmoud, Omar

2018-02-01

Nonretroperitoneal abdominal soft tissue sarcoma (NRA-STS) is a rare disease with limited data supporting its management. Our study aimed to reveal the utilization patterns of adjuvant therapy and its potential survival benefits using the National Cancer Data Base. The analysis included patients with resected high-grade NRA-STS. Chi-square analysis was used to evaluate distribution of patient and tumor-related factors within treatment groups. The Kaplan-Meier and Cox proportional hazards model were utilized to evaluate overall survival according to treatment approach. Multivariate analysis was used to determine the impact of these factors on patients' outcome. Matched propensity score analysis was implemented to control for imbalance of confounding variables. At median follow-up of 49 months, 5-year overall survival improved from 46% without adjuvant radiation therapy to 52% (P = 0.009) with radiotherapy delivery with a 30% reduction in hazard of death (95% confidence interval = 0.58-0.84). On multivariate analysis, age <50, tumor <8 cm, negative margins and radiotherapy delivery were significant predictors of improved survival. Chemotherapy was not associated with significant survival improvement (Hazard Ratios [HR]: 0.89, P = 0.28). Adjuvant radiotherapy was associated with improved survival in high-grade NRA-STS. Chemotherapy was not associated with a survival improvement; however, further studies are needed to refine treatment strategies. © 2017 John Wiley & Sons Australia, Ltd.

Covariate analysis of bivariate survival data

Energy Technology Data Exchange (ETDEWEB)

Bennett, L.E.

1992-01-01

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

Denial-of-service attack detection based on multivariate correlation analysis

NARCIS (Netherlands)

Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

2011-01-01

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

Marital Status and Survival in Patients with Carcinoid Tumors.

Science.gov (United States)

Greenleaf, Erin K; Cooper, Amanda B; Hollenbeak, Christopher S

2016-01-01

Marital status is a known prognostic factor in overall and disease-specific survival in several types of cancer. The impact of marital status on survival in patients with carcinoid tumors remains unknown. We hypothesized that married patients have higher rates of survival than similar unmarried patients with carcinoid tumors. Using the Surveillance, Epidemiology, and End Results database, we identified 23,126 people diagnosed with a carcinoid tumor between 2000 and 2011 and stratified them according to marital status. Univariate and multivariable analyses were performed to compare the characteristics and outcomes between patient cohorts. Overall and cancer-related survival were analyzed using the Kaplan-Meier method. Multivariable survival analyses were performed using Cox proportional hazards models (hazards ratio [HR]), controlling for demographics and tumor-related and treatment-related variables. Propensity score analysis was performed to determine surgical intervention distributions among married and unmarried (ie, single, separated, divorced, widowed) patients. Marital status was significantly related to both overall and cancer-related survival in patients with carcinoid tumors. Divorced and widowed patients had worse overall survival (HR, 1.33 [95% confidence interval {CI}, 1.08-1.33] and 1.34 [95% CI, 1.22-1.46], respectively) and cancer-related survival (HR, 1.15 [95% CI, 1.00-1.31] and 1.15 [95% CI, 1.03-1.29], respectively) than married patients over five years. Single and separated patients had worse overall survival (HR, 1.20 [95% CI, 1.08-1.33] and 1.62 [95% CI, 1.25-2.11], respectively) than married patients over five years, but not worse cancer-related survival. Unmarried patients were more likely than matched married patients to undergo definitive surgical intervention (62.67% vs 53.11%, respectively, P married patients have a survival advantage after diagnosis of any carcinoid tumor, potentially reflecting better social support and financial means

Hierarchical multivariate covariance analysis of metabolic connectivity.

Science.gov (United States)

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

CASAS: Cancer Survival Analysis Suite, a web based application.

Science.gov (United States)

Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

2017-01-01

We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

Small cell lung cancer with and without superior vena cava syndrome: a multivariate analysis of prognostic factors in 408 cases

International Nuclear Information System (INIS)

Wuerschmidt, Florian; Buenemann, Henry; Heilmann, Hans-Peter

1995-01-01

Purpose: Patients with small cell lung cancer (SCLC) and superior vena cava syndrome (SVCS) are widely believed to have a grave prognosis. The purpose of this study was to determine the prognosis of patients with SCLC and SVCS as compared to SCLC without SVCS. Methods and Materials: A retrospective analysis of 408 cases of SCLC ± SVCS was performed. Three-hundred and sixty showed no clinical signs of SVCS and 43 (11%) had SVCS; in 5 patients no adequate information was available about clinical signs of SVCS. All patients were classified as limited disease cases. About 98% received chemotherapy usually as the first treatment followed by radiotherapy. A median total dose of 46 Gy (range 30 to 70 Gy) was given at 2.0 Gy per fraction five times weekly. A prophylactic cranial irradiation was applied if a complete remission was achieved after chemotherapy or after 30 Gy of irradiation. Kaplan-Meier survival curves are shown and comparisons were made by the log-rank and the Gehan/Wilcoxon test. To adjust for prognostic factors, a proportional hazards analysis was done. Results: Patients without SVCS had 5-year survival rates (± SE) and a median survival time (MST; 95% confidence intervals) of 11% ± 2% and 13.7 months (12.7-14.5) in UICC Stage I to III; in Stage III the figures were 9% ± 2% and 12.6 months (11.2-13.7). In comparison, SCLC with SVCS had 5-year survival rates of 15% ± 7% and MST of 16.1 months (13.8-20.5). The difference was significant in univariate analysis (Stage III disease: p 0.008 by the log-rank test). In a multivariate analysis of all patients, Stage (Stage I + II > III; p = 0.0003), SVCS (yes > no; p = 0.005), and Karnofsky performance status (≤ 70 < 80-100%; p = 0.008) were of significant importance. Conclusions: SVCS is a favorable prognostic sign in SCLC. The treatment should be curatively intended

Multivariate performance reliability prediction in real-time

International Nuclear Information System (INIS)

Lu, S.; Lu, H.; Kolarik, W.J.

2001-01-01

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

DEFF Research Database (Denmark)

Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

2006-01-01

Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

The prognostic significance of midline shift at presentation on survival in patients with glioblastoma multiforme

International Nuclear Information System (INIS)

Gamburg, Eugene S.; Regine, William F.; Patchell, Roy A.; Strottmann, James M.; Mohiuddin, Mohammed; Young, A. Byron

2000-01-01

Purpose: While patients with glioblastoma multiforme (GBM) who present with midline shift have a presumably worse prognosis, there is little literature evaluating the prognostic significance of this presentation in multivariate analysis in the context of other known prognostic factors. Methods and Materials: From March 1981 to September 1993, 219 patients underwent irradiation for intracranial glioma at our institution. One hundred fourteen patients with a diagnosis of a primary GBM were analyzed for the influence of the presence of midline shift at diagnosis on survival with respect to other known prognostic factors, including age, Karnofsky performance status (KPS), and extent of surgery. Eighty-five patients (74%) presented with midline shift. Surgical treatment consisted of subtotal/total resection in 86 patients (75%). Among patients presenting with midline shift, 68 (80%) underwent subtotal/total resection before irradiation. Results: Multivariate analysis of the entire cohort of patients found none of the potential prognostic factors analyzed to significantly influence survival. The overall median survival was 6 months. However, when multivariate analysis was limited to patients with a KPS of ≥ 70, only the presence of midline shift and age were found to significantly influence survival. Patients with a KPS ≥ 70 and with midline shift present at diagnosis had a median survival of 8 months, as compared to 14 months for those not having midline shift at presentation (p = 0.04). Patients with a KPS ≥ 70 and age > 50 years had a median survival of 5 months as compared to 11 months for those ≤ 50 (p 0.02). Conclusion: In this series, where 80% of patients who presented with a midline shift underwent decompressive resection of GBM before irradiation, the presence of midline shift at diagnosis remained an independent prognostic factor influencing survival among good performance status patients. While the role of decompressive surgery in this setting is

Reassessment of the relationship between M-protein decrement and survival in multiple myeloma.

OpenAIRE

Palmer, M.; Belch, A.; Hanson, J.; Brox, L.

1989-01-01

The relationship between percentage M-protein decrement and survival is assessed in 134 multiple myeloma patients. The correlation did not achieve statistical significance (P = 0.069). Multivariate analysis using the Cox proportional hazards model, including a number of previously recognised prognostic factors, showed only percentage M-protein decrement, creatinine and haemoglobin to be significantly correlated with survival. However, the R'-statistic for each of these variables was low, indi...

Survival prediction model for postoperative hepatocellular carcinoma patients.

Science.gov (United States)

Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

2017-09-01

This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

Multivariate statistical analysis of major and trace element data for ...

African Journals Online (AJOL)

Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

Pre-therapeutic factors for predicting survival after radioembolization: a single-center experience in 389 patients

International Nuclear Information System (INIS)

Paprottka, K.J.; Schoeppe, F.; Ingrisch, M.; Ruebenthaler, J.; Sommer, N.N.; Paprottka, P.M.; Toni, E. de; Ilhan, H.; Zacherl, M.; Todica, A.

2017-01-01

To determine pre-therapeutic predictive factors for overall survival (OS) after yttrium (Y)-90 radioembolization (RE). We retrospectively analyzed the pre-therapeutic characteristics (sex, age, tumor entity, hepatic tumor burden, extrahepatic disease [EHD] and liver function [with focus on bilirubin and cholinesterase level]) of 389 consecutive patients with various refractory liver-dominant tumors (hepatocellular carcinoma [HCC], cholangiocarcinoma [CCC], neuroendocrine tumor [NET], colorectal cancer [CRC] and metastatic breast cancer [MBC]), who received Y-90 radioembolization for predicting survival. Predictive factors were selected by univariate Cox regression analysis and subsequently tested by multivariate analysis for predicting patient survival. The median OS was 356 days (95% CI 285-427 days). Stable disease was observed in 132 patients, an objective response in 71 (one of which was complete remission) and progressive disease in 122. The best survival rate was observed in patients with NET, and the worst in patients with MBC. In the univariate analyses, extrahepatic disease (P < 0.001), large tumor burden (P = 0.001), high bilirubin levels (>1.9 mg/dL, P < 0.001) and low cholinesterase levels (CHE <4.62 U/I, P < 0.001) at baseline were significantly associated with poor survival. Tumor entity, tumor burden, extrahepatic disease and CHE were confirmed in the multivariate analysis as independent predictors of survival. Sex, applied RE dose and age had no significant influence on OS. Pre-therapeutic baseline bilirubin and CHE levels, extrahepatic disease and hepatic tumor burden are associated with patient survival after RE. Such parameters may be used to improve patient selection for RE of primary or metastatic liver tumors. (orig.)

Pre-therapeutic factors for predicting survival after radioembolization: a single-center experience in 389 patients

Energy Technology Data Exchange (ETDEWEB)

Paprottka, K.J.; Schoeppe, F.; Ingrisch, M.; Ruebenthaler, J.; Sommer, N.N.; Paprottka, P.M. [LMU - University of Munich, Department of Clinical Radiology, Munich (Germany); Toni, E. de [LMU - University of Munich, Department of Hepatology, Munich (Germany); Ilhan, H.; Zacherl, M.; Todica, A. [LMU - University of Munich, Department of Nuclear Medicine, Munich (Germany)

2017-07-15

To determine pre-therapeutic predictive factors for overall survival (OS) after yttrium (Y)-90 radioembolization (RE). We retrospectively analyzed the pre-therapeutic characteristics (sex, age, tumor entity, hepatic tumor burden, extrahepatic disease [EHD] and liver function [with focus on bilirubin and cholinesterase level]) of 389 consecutive patients with various refractory liver-dominant tumors (hepatocellular carcinoma [HCC], cholangiocarcinoma [CCC], neuroendocrine tumor [NET], colorectal cancer [CRC] and metastatic breast cancer [MBC]), who received Y-90 radioembolization for predicting survival. Predictive factors were selected by univariate Cox regression analysis and subsequently tested by multivariate analysis for predicting patient survival. The median OS was 356 days (95% CI 285-427 days). Stable disease was observed in 132 patients, an objective response in 71 (one of which was complete remission) and progressive disease in 122. The best survival rate was observed in patients with NET, and the worst in patients with MBC. In the univariate analyses, extrahepatic disease (P < 0.001), large tumor burden (P = 0.001), high bilirubin levels (>1.9 mg/dL, P < 0.001) and low cholinesterase levels (CHE <4.62 U/I, P < 0.001) at baseline were significantly associated with poor survival. Tumor entity, tumor burden, extrahepatic disease and CHE were confirmed in the multivariate analysis as independent predictors of survival. Sex, applied RE dose and age had no significant influence on OS. Pre-therapeutic baseline bilirubin and CHE levels, extrahepatic disease and hepatic tumor burden are associated with patient survival after RE. Such parameters may be used to improve patient selection for RE of primary or metastatic liver tumors. (orig.)

Overhydration, Cardiac Function and Survival in Hemodialysis Patients.

Science.gov (United States)

Onofriescu, Mihai; Siriopol, Dimitrie; Voroneanu, Luminita; Hogas, Simona; Nistor, Ionut; Apetrii, Mugurel; Florea, Laura; Veisa, Gabriel; Mititiuc, Irina; Kanbay, Mehmet; Sascau, Radu; Covic, Adrian

2015-01-01

Chronic subclinical volume overload occurs very frequently and may be ubiquitous in hemodialysis (HD) patients receiving the standard thrice-weekly treatment. It is directly associated with hypertension, increased arterial stiffness, left ventricular hipertrophy, heart failure, and eventually, higher mortality and morbidity. We aimed to assess for the first time if the relationship between bioimpedance assessed overhydration and survival is maintained when adjustments for echocardiographic parameters are considered. A prospective cohort trial was conducted to investigate the impact of overhydration on all cause mortality and cardiovascular events (CVE), by using a previously reported cut-off value for overhydration and also investigating a new cut-off value derived from our analysis of this specific cohort. The body composition of 221 HD patients from a single center was assessed at baseline using bioimpedance. In 157 patients supplemental echocardiography was performed (echocardiography subgroup). Comparative survival analysis was performed using two cut-off points for relative fluid overload (RFO): 15% and 17.4% (a value determined by statistical analysis to have the best predictive value for mortality in our cohort). In the entire study population, patients considered overhydrated (using both cut-offs) had a significant increased risk for all-cause mortality in both univariate (HR = 2.12, 95%CI = 1.30-3.47 for RFO>15% and HR = 2.86, 95%CI = 1.72-4.78 for RFO>17.4%, respectively) and multivariate (HR = 1.87, 95%CI = 1.12-3.13 for RFO>15% and HR = 2.72, 95%CI = 1.60-4.63 for RFO>17.4%, respectively) Cox survival analysis. In the echocardiography subgroup, only the 17.4% cut-off remained associated with the outcome after adjustment for different echocardiographic parameters in the multivariate survival analysis. The number of CVE was significantly higher in overhydrated patients in both univariate (HR = 2.46, 95%CI = 1.56-3.87 for RFO >15% and HR = 3.67, 95%CI = 2

Overhydration, Cardiac Function and Survival in Hemodialysis Patients.

Directory of Open Access Journals (Sweden)

Mihai Onofriescu

Full Text Available Chronic subclinical volume overload occurs very frequently and may be ubiquitous in hemodialysis (HD patients receiving the standard thrice-weekly treatment. It is directly associated with hypertension, increased arterial stiffness, left ventricular hipertrophy, heart failure, and eventually, higher mortality and morbidity. We aimed to assess for the first time if the relationship between bioimpedance assessed overhydration and survival is maintained when adjustments for echocardiographic parameters are considered.A prospective cohort trial was conducted to investigate the impact of overhydration on all cause mortality and cardiovascular events (CVE, by using a previously reported cut-off value for overhydration and also investigating a new cut-off value derived from our analysis of this specific cohort. The body composition of 221 HD patients from a single center was assessed at baseline using bioimpedance. In 157 patients supplemental echocardiography was performed (echocardiography subgroup. Comparative survival analysis was performed using two cut-off points for relative fluid overload (RFO: 15% and 17.4% (a value determined by statistical analysis to have the best predictive value for mortality in our cohort.In the entire study population, patients considered overhydrated (using both cut-offs had a significant increased risk for all-cause mortality in both univariate (HR = 2.12, 95%CI = 1.30-3.47 for RFO>15% and HR = 2.86, 95%CI = 1.72-4.78 for RFO>17.4%, respectively and multivariate (HR = 1.87, 95%CI = 1.12-3.13 for RFO>15% and HR = 2.72, 95%CI = 1.60-4.63 for RFO>17.4%, respectively Cox survival analysis. In the echocardiography subgroup, only the 17.4% cut-off remained associated with the outcome after adjustment for different echocardiographic parameters in the multivariate survival analysis. The number of CVE was significantly higher in overhydrated patients in both univariate (HR = 2.46, 95%CI = 1.56-3.87 for RFO >15% and HR = 3

SAMSN1 is highly expressed and associated with a poor survival in glioblastoma multiforme.

Directory of Open Access Journals (Sweden)

Yong Yan

Full Text Available OBJECTIVES: To study the expression pattern and prognostic significance of SAMSN1 in glioma. METHODS: Affymetrix and Arrystar gene microarray data in the setting of glioma was analyzed to preliminarily study the expression pattern of SAMSN1 in glioma tissues, and Hieratical clustering of gene microarray data was performed to filter out genes that have prognostic value in malignant glioma. Survival analysis by Kaplan-Meier estimates stratified by SAMSN1 expression was then made based on the data of more than 500 GBM cases provided by The Cancer Genome Atlas (TCGA project. At last, we detected the expression of SAMSN1 in large numbers of glioma and normal brain tissue samples using Tissue Microarray (TMA. Survival analysis by Kaplan-Meier estimates in each grade of glioma was stratified by SAMSN1 expression. Multivariate survival analysis was made by Cox proportional hazards regression models in corresponding groups of glioma. RESULTS: With the expression data of SAMSN1 and 68 other genes, high-grade glioma could be classified into two groups with clearly different prognoses. Gene and large sample tissue microarrays showed high expression of SAMSN1 in glioma particularly in GBM. Survival analysis based on the TCGA GBM data matrix and TMA multi-grade glioma dataset found that SAMSN1 expression was closely related to the prognosis of GBM, either PFS or OS (P<0.05. Multivariate survival analysis with Cox proportional hazards regression models confirmed that high expression of SAMSN1 was a strong risk factor for PFS and OS of GBM patients. CONCLUSION: SAMSN1 is over-expressed in glioma as compared with that found in normal brains, especially in GBM. High expression of SAMSN1 is a significant risk factor for the progression free and overall survival of GBM.

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.

Science.gov (United States)

Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh

2018-03-01

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

Blood transfusion and survival after surgery for Stage I and II breast cancer

International Nuclear Information System (INIS)

Herman, K.; Kolodziejski, L.

1993-01-01

The records of 690 Stage I and II breast cancer patients (31% of them with transfusions), who underwent mastectomy with axillary dissection were examined whether perioperative blood transfusion might be detrimental to survival. The overall 5- and 1-year survival rates for 477 patients who had not received transfusions were 75% and 63% respectively, compared with 66% and 49% for those who had transfusions (p=0.005). There was no significant difference between the group in any other of the most important prognostic factors. An analysis of the subpopulation of patients with favorable prognostic factors yielded similar results. A multivariate analysis indicated that blood transfusion was one of the four variables significantly related to survival. (author)

Determinants of malignant pleural mesothelioma survival and burden of disease in France: a national cohort analysis.

Science.gov (United States)

Chouaid, Christos; Assié, Jean Baptiste; Andujar, Pascal; Blein, Cecile; Tournier, Charlène; Vainchtock, Alexandre; Scherpereel, Arnaud; Monnet, Isabelle; Pairon, Jean Claude

2018-04-01

This study was undertaken to determine the healthcare burden of malignant pleural mesothelioma (MPM) in France and to analyze its associations with socioeconomic deprivation, population density, and management outcomes. A national hospital database was used to extract incident MPM patients in years 2011 and 2012. Cox models were used to analyze 1- and 2-year survival according to sex, age, co-morbidities, management, population-density index, and social deprivation index. The analysis included 1,890 patients (76% men; age: 73.6 ± 10.0 years; 84% with significant co-morbidities; 57% living in urban zones; 53% in highly underprivileged areas). Only 1% underwent curative surgical procedure; 65% received at least one chemotherapy cycle, 72% of them with at least one pemetrexed and/or bevacizumab administration. One- and 2-year survival rates were 64% and 48%, respectively. Median survival was 14.9 (95% CI: 13.7-15.7) months. The mean cost per patient was 27,624 ± 17,263 euros (31% representing pemetrexed and bevacizumab costs). Multivariate analyses retained men, age >70 years, chronic renal failure, chronic respiratory failure, and never receiving pemetrexed as factors of poor prognosis. After adjusting the analysis to age, sex, and co-morbidities, living in rural/semi-rural area was associated with better 2-year survival (HR: 0.83 [95% CI: 0.73-0.94]; P < 0.01); social deprivation index was not significantly associated with survival. With approximately 1,000 new cases per year in France, MPMs represents a significant national health care burden. Co-morbidities, sex, age, and living place appear to be significant factors of prognosis. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

Science.gov (United States)

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis

NARCIS (Netherlands)

Brindle, R. C.; Ginty, A. T.; Jones, A.; Phillips, A. C.; Roseboom, T. J.; Carroll, D.; Painter, R. C.; de Rooij, S. R.

2016-01-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure

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

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Daniel Willian Lustosa de Sousa

2015-08-01

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

Multivariate analysis between air pollutants and meteorological variables in Seoul

International Nuclear Information System (INIS)

Kim, J.; Lim, J.

2005-01-01

Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

Solitary plasmacytoma: population-based analysis of survival trends and effect of various treatment modalities in the USA.

Science.gov (United States)

Thumallapally, Nishitha; Meshref, Ahmed; Mousa, Mohammed; Terjanian, Terenig

2017-01-05

Solitary plasmacytoma (SP) is a localized neoplastic plasma cell disorder with an annual incidence of less than 450 cases. Given the rarity of this disorder, it is difficult to conduct large-scale population studies. Consequently, very limited information on the disorder is available, making it difficult to estimate the incidence and survival rates. Furthermore, limited information is available on the efficacy of various treatment modalities in relation to primary tumor sites. The data for this retrospective study were drawn from the Surveillance, Epidemiology and End Results (SEER) database, which comprises 18 registries; patient demographics, treatment modalities and survival rates were obtained for those diagnosed with SP from 1998 to 2007. Various prognostic factors were analyzed via Kaplan-Meier analysis and log-rank test, with 5-year relative survival rate defined as the primary outcome of interest. Cox regression analysis was employed in the multivariate analysis. The SEER search from 1998 to 2007 yielded records for 1691 SP patients. The median age at diagnosis was 63 years. The patient cohort was 62.4% male, 37.6% female, 80% Caucasian, 14.6% African American and 5.4% other races. Additionally, 57.8% had osseous plasmacytoma, and 31.9% had extraosseous involvement. Unspecified plasmacytoma was noted in 10.2% of patients. The most common treatment modalities were radiotherapy (RT) (48.8%), followed by combination surgery with RT (21.2%) and surgery alone (11.6%). Univariate analysis of prognostic factors revealed that the survival outcomes were better for younger male patients who received RT with surgery (p multiple myeloma (MM) was noted in 551 patients. Age >60 years was associated with a lower 5-year survival in patients who progressed to MM compared to those who were diagnosed initially with MM (15.1 vs 16.6%). Finally, those who received RT and progressed to MM still had a higher chance of survival than those who were diagnosed with MM initially and

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

Energy Technology Data Exchange (ETDEWEB)

Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

International Nuclear Information System (INIS)

Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

2013-01-01

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

The relation between lymph node status and survival in Stage I-III colon cancer

DEFF Research Database (Denmark)

Lykke, J.; Roikjær, Ole; Jess, P.

2013-01-01

Aim: This study involved a large nationwide Danish cohort to evaluate the hypothesis that a high lymph node harvest has a positive effect on survival in curative resected Stage I-III colon cancer and a low lymph node ratio has a positive effect on survival in Stage III colon cancer. Method......: Analysis of overall survival was conducted using a nationwide Danish cohort of patients treated with curative resection of Stage I-III colon cancer. All 8901 patients in Denmark diagnosed with adenocarcinoma of the colon and treated with curative resection in the period 2003-2008 were identified from...... independent prognostic factors in multivariate analysis. Conclusion: High lymph node count was associated with improved overall survival in colon cancer. Lymph node ratio was superior to N-stage in differentiating overall survival in Stage III colon cancer. Stage migration was observed....

Multivariate analysis of data in sensory science

CERN Document Server

Naes, T; Risvik, E

1996-01-01

The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group

DEFF Research Database (Denmark)

Christophersen, J; Menné, T; Tanghøj, P

1989-01-01

The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba......-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis....

Decomposition of multivariate phenotypic means in multigroup genetic covarinace structure analysis

NARCIS (Netherlands)

Dolan, C.V.; Molenaar, P.C.M.; Boomsma, D.I.

1992-01-01

Uses D. Sorbom's (1974) method to study differences in latent means in multivariate twin data. By restricting the analysis to a comparison between groups, the results pertain only to the additive contributions of common genetic and environmental factors to the deviation of the group means from what

Better midterm survival in women after transcatheter aortic valve implantation.

Science.gov (United States)

Takagi, Hisato; Umemoto, Takuya

2017-08-01

In previous meta-analyses demonstrating better midterm overall survival in women undergoing transcatheter aortic valve implantation (TAVI), unadjusted risk and odds ratios were combined. To determine whether female gender is independently associated with better survival after TAVI, we performed a meta-analysis pooling adjusted hazard ratios (HRs) based on multivariate Cox proportional hazard regression. MEDLINE and EMBASE were searched through September 2015 using PubMed and OVID. Studies considered for inclusion met the following criteria: the study population was patients undergoing TAVI; and main outcomes included midterm (mean or median ≥6 months) overall survival or all-cause mortality in women and men. An unadjusted and/or adjusted HR of all-cause mortality for women versus men was abstracted from each individual study. Of 1347 potentially relevant articles screened initially, 16 reports of eligible studies were identified and included. A primary meta-analysis of the 9 adjusted HRs demonstrated a significantly better midterm overall survival in women than men (N.=6891; HR=0.80; 95% confidence interval [CI]: 0.65 to 0.97; P=0.03). A secondary meta-analysis adding 5 statistically non-significant unadjusted HR also indicated better survival in women (N.=8645; HR=0.83; 95% CI: 0.72 to 0.96; P=0.01). Although statistical tests for the primary meta-analysis revealed funnel plot asymmetry in favor of women, the secondary meta-analysis produced a symmetrical funnel plot. Female gender may be independently associated with better midterm overall survival after TAVI.

Marital Status and Survival in Patients with Carcinoid Tumors

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Erin K. Greenleaf

2016-01-01

Full Text Available Background Marital status is a known prognostic factor in overall and disease-specific survival in several types of cancer. The impact of marital status on survival in patients with carcinoid tumors remains unknown. We hypothesized that married patients have higher rates of survival than similar unmarried patients with carcinoid tumors. Methods Using the Surveillance, Epidemiology, and End Results database, we identified 23,126 people diagnosed with a carcinoid tumor between 2000 and 2011 and stratified them according to marital status. Univariate and multivariable analyses were performed to compare the characteristics and outcomes between patient cohorts. Overall and cancer-related survival were analyzed using the Kaplan–Meier method. Multivariable survival analyses were performed using Cox proportional hazards models (hazards ratio [HR], controlling for demographics and tumor-related and treatment-related variables. Propensity score analysis was performed to determine surgical intervention distributions among married and unmarried (ie, single, separated, divorced, widowed patients. Results Marital status was significantly related to both overall and cancer-related survival in patients with carcinoid tumors. Divorced and widowed patients had worse overall survival (HR, 1.33 [95% confidence interval {CI}, 1.08–1.33] and 1.34 [95% CI, 1.22–1.46], respectively and cancer-related survival (HR, 1.15 [95% CI, 1.00–1.31] and 1.15 [95% CI, 1.03–1.29], respectively than married patients over five years. Single and separated patients had worse overall survival (HR, 1.20 [95% CI, 1.08–1.33] and 1.62 [95% CI, 1.25–2.11], respectively than married patients over five years, but not worse cancer-related survival. Unmarried patients were more likely than matched married patients to undergo definitive surgical intervention (62.67% vs 53.11%, respectively, P < 0.0001. Conclusions Even after controlling for other prognostic factors, married patients

Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

International Nuclear Information System (INIS)

Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

2008-01-01

Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

Mathematical Methods in Survival Analysis, Reliability and Quality of Life

CERN Document Server

Huber, Catherine; Mesbah, Mounir

2008-01-01

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

Clinicopathologic Features and Survival of Breast Cancer Subtypes in Northeast Iran

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Soodabeh Shahidsales

2018-01-01

Full Text Available Background: Breast cancer can be categorized into different histopathological subtypes based on gene expression profiles. This study aims to evaluate the clinicopathological features and overall survival of various subtypes of breast cancer to assist diagnosis and guide treatment. Methods: The clinicopathologic features of 1095 patients with breast cancer diagnosed over a 10–year period between 2001 and 2011 were analyzed. The Kaplan–Meier method was used to analyze disease-free survival and overall survival. Calculation of the hazard ratio was conducted by multivariate Cox regression. Results: According to the clinicopathologic characteristics of 1095 cases, there were 42% luminal A subtype, 19.2% luminal B, 23% triple negative, and 15% HER2+. The lowest (46.88±12.59 years and highest (50.54±12.32 years mean ages were in the triple negative and HER2+ groups, respectively. There was a significant correlation between histology subtype and age, BMI, lymph node, type of surgery, and stage of disease. There was significantly shorter overall survival and disease free survival in HER2+ breast cancer patients (P<0.001. Multivariate analysis showed that age had the highest hazard ratio of 2.481 (95% Confidence Interval: 1.375-4.477. Conclusion: The results of this study showed the importance of clinicopathological studies of molecular types which help early diagnosis and identification of the best strategy to treat breast cancer.

Identification of multivariate models for noise analysis of nuclear plant

International Nuclear Information System (INIS)

Zwingelstein, G.C.; Upadhyaya, B.R.

1979-01-01

During the normal operation of a pressurized water reactor, neutron noise analysis with multivariate autoregressive procedures in a valuable diagnostic tool to extract dynamic characteristics for incipient failure detection. The first part of the paper will describe in details the equations for estimating the multivariate autoregressive model matrices and the structure of various matrices. The matrices are estimated by solving a set of matrix operations, called Yule-Walker equations. The selection of optimal model order will also be discussed. Once the optimal parameter set is obtained, simple and fast calculations are used to determine the auto power spectral density, cross spectra, coherence function, phase. In addition the spectra may be decomposed into components being contributed from different noise sources. An application using neutron flux data collected on a nuclear plant will illustrate the efficiency of the method

Multivariate analysis of prognostic factors in male breast cancer in Serbia.

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Sipetic-Grujicic, Sandra Branko; Murtezani, Zafir Hajdar; Neskovic-Konstatinovic, Zora Borivoje; Marinkovic, Jelena Milutin; Kovcin, Vladimir Nikola; Andric, Zoran Gojko; Kostic, Sanja Vladeta; Ratkov, Isidora Stojan; Maksimovic, Jadranka Milutin

2014-01-01

The aim of this study was to analyze the demographic and clinical characteristics of male breast cancer patients in Serbia, and furthermore to determine overall survival and predictive factors for prognosis. In the period of 1996-2006 histopathological diagnosis of breast cancer was made in 84 males at the Institute for Oncology and Radiology of Serbia. For statistical analyses the Kaplan-Meier method, long-rank test and Cox proportional hazards regression model were used. The mean age at diagnosis with breast cancer was 64.3±10.5 years with a range from 35-84 years. Nearly 80% of the tumors showed ductal histology. About 44% had early tumor stages (I and II) whereas 46.4% and 9.5% of the male exhibited stages III and IV, respectively. Only 7.1% of male patients were grade one. One-fifth of all patients had tumors measuring ≤2 cm, and 14.3% larger than 5 cm. Lymph node metastasis was recorded in 40.4% patients and 47% relapse. Estrogen and progesterone receptor expression was positive in 66.7% and 58.3%, respectively. Among 14.3% of individuals tumor was HER2 positive. About two-thirds of all male patients had radical mastectomy (66.7%). Adjuvant hormonal (tamoxifene), systematic chemotherapy (CMF or FAC) and adjuvant radiotherapy were given to 59.5%, 35.7% and 29.8% patients respectively. Overall survival rates at five and ten years for male breast cancer were 55.0% and 43.9%, respectively. According to the multivariate Cox regression predictive model, a lower initial disease stage, a lower tumor grade, application of adjuvant hormone therapy and no relapse occurrence were significant independent predictors for good overall survival. Results of the treatment would be better if disease is discovered earlier and therefore health education and screening are an imperative in solving this problem.

Handbook of univariate and multivariate data analysis with IBM SPSS

CERN Document Server

Ho, Robert

2013-01-01

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

Breast cancer data analysis for survivability studies and prediction.

Science.gov (United States)

Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

2018-03-01

Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

Association of collagen architecture with glioblastoma patient survival.

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Pointer, Kelli B; Clark, Paul A; Schroeder, Alexandra B; Salamat, M Shahriar; Eliceiri, Kevin W; Kuo, John S

2017-06-01

OBJECTIVE Glioblastoma (GBM) is the most malignant primary brain tumor. Collagen is present in low amounts in normal brain, but in GBMs, collagen gene expression is reportedly upregulated. However, to the authors' knowledge, direct visualization of collagen architecture has not been reported. The authors sought to perform the first direct visualization of GBM collagen architecture, identify clinically relevant collagen signatures, and link them to differential patient survival. METHODS Second-harmonic generation microscopy was used to detect collagen in a GBM patient tissue microarray. Focal and invasive GBM mouse xenografts were stained with Picrosirius red. Quantitation of collagen fibers was performed using custom software. Multivariate survival analysis was done to determine if collagen is a survival marker for patients. RESULTS In focal xenografts, collagen was observed at tumor brain boundaries. For invasive xenografts, collagen was intercalated with tumor cells. Quantitative analysis showed significant differences in collagen fibers for focal and invasive xenografts. The authors also found that GBM patients with more organized collagen had a longer median survival than those with less organized collagen. CONCLUSIONS Collagen architecture can be directly visualized and is different in focal versus invasive GBMs. The authors also demonstrate that collagen signature is associated with patient survival. These findings suggest that there are collagen differences in focal versus invasive GBMs and that collagen is a survival marker for GBM.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

International Nuclear Information System (INIS)

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-01-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component

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

CERN Document Server

Tableman, Mara

2003-01-01

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

Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

Science.gov (United States)

Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

2012-07-01

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

[Survival analysis with competing risks: estimating failure probability].

Science.gov (United States)

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

Incidence, treatment, and survival patterns for sacral chordoma in the United States, 1974-2011

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Esther Yu

2016-09-01

Full Text Available IntroductionSacral chordomas represent one half of all chordomas, a rare neoplasm of notochordal remnants. Current NCCN guidelines recommend surgical resection with or without adjuvant radiotherapy, or definitive radiation for unresectable cases. Recent advances in radiation for chordomas include conformal photon and proton beam radiation. We investigated incidence, treatment, and survival outcomes to observe any trends in response to improvements in surgical and radiation techniques over a near 40 year time period.Materials and Methods345 microscopically confirmed cases of sacral chordoma were identified between 1974 and 2011 from the Surveillance, Epidemiology, and End Results (SEER program of the National Cancer Institute. Cases were divided into three cohorts by calendar year, 1974-1989, 1990-1999, and 2000-2011, as well as into two groups by age less than or equal to 65 versus greater than 65 to investigate trends over time and age via Chi-square analysis. Kaplan-Meier analyses were performed to determine effects of treatment on survival. Multivariate Cox regression analysis was performed to determine predictors of overall survival.Results5-year overall survival for the entire cohort was 60.0%. Overall survival correlated significantly with treatment modality, with 44% surviving at 5 years with no treatment, 52% with radiation alone, 82% surgery alone, and 78% surgery and radiation (p<.001. Age greater than 65 was significantly associated with non-surgical management with radiation alone or no treatment (p<.001. Relatively fewer patients received radiation between 2000 and 2011 compared to prior time periods (p=.03 versus surgery, for which rates which did not vary significantly over time (p=.55. However, 5-year overall survival was not significantly different by time period. Age group and treatment modality were predictive for overall survival on multivariate analysis (p<.001. ConclusionSurgery remains an important component in the

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

Science.gov (United States)

Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

2013-05-07

To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

Risk factors and a prognostic score for survival after autologous stem-cell transplantation for relapsed or refractory Hodgkin lymphoma

DEFF Research Database (Denmark)

Bröckelmann, P J; Müller, H; Casasnovas, O

2017-01-01

study (n = 1045), precise and valid risk prognostication in HL patients undergoing ASCT can be achieved with five easily available clinical RFs. The proposed prognostic score hence allows reliable stratification of patients for innovative therapeutic approaches in clinical practice and future trials...... therapeutic approaches, we investigated a comprehensive set of risk factors (RFs) for survival after ASCT. Methods: In this multinational prognostic multivariable modeling study, 23 potential RFs were retrospectively evaluated in HL patients from nine prospective trials with multivariable Cox proportional...... of potential RFs had significant impact on progression-free survival (PFS) with hazard ratios (HR) ranging from 1.39 to 2.22. The multivariable analysis identified stage IV disease, time to relapse ≤3 months, ECOG performance status ≥1, bulk ≥5 cm and inadequate response to salvage chemotherapy [

Direct Survival Analysis: a new stock assessment method

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Eduardo Ferrandis

2007-03-01

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

THROMBOCYTOSIS AS PROGNOSTIC FACTOR FOR SURVIVAL IN PATIENTS WITH ADVANCED NON SMALL CELL LUNG CANCER TREATED WITH FIRST- LINE CHEMOTHERAPY.

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Deyan Davidov

2014-12-01

Full Text Available Objective: The aim of this study was to evaluate elevated platelet count as a prognostic factor for survival in patients with advanced (stage IIIB/ IV non- small cell lung cancer (NSCLC receiving first- line chemotherapy. Methods: From 2005 to 2009 three hundreds forty seven consecutive patients with stage IIIB or IV NSCLC, treated in Department of Medical Oncology, UMHAT "Dr Georgi Stranski" entered the study. The therapeutic regimens included intravenous administration of platinum- based doublets. Survival analysis was evaluated by Kaplan- Meier test. The influence of pretreatment thrombocytosis as prognostic factor for survival was analyzed using multivariate stepwise Cox regression analyses. Results: Elevated platelet counts were found in 78 patients. The overall survival for patients without elevated platelet counts was 9,6 months versus 6,9 months for these with thrombocytosis. In multivariate analysis as independent poor prognostic factors were identified: stage, performance status and elevated platelet counts. Conclusions: These results indicated that platelet counts as well as some clinical pathologic characteristics could be useful prognostic factors in patients with unresectable NSCLC.

Neyman, Markov processes and survival analysis.

Science.gov (United States)

Yang, Grace

2013-07-01

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

Multivariate analysis of the prognostic factors of squamous cell cervical cancer treated by radical hysterectomy or combined radiation therapy

International Nuclear Information System (INIS)

Coelho, Francisco Ricardo Gualda; Kowalski, Luiz Paulo; Abrao, Fauzer Simao; Franco, Eduardo Luiz; Zeferino, Luiz Carlos; Brentani, Maria Mitzi

1996-01-01

Six hundred and nine cases of invasive squamous cell carcinoma of the cervix uteri in a retrospective analysis (1953-1982) at the A.C. Camargo Hospital, Antonio Prudente Foundation, Sao Paulo, Brazil. The patients were submitted to radical surgery and radiation therapy, individually or in combination. A multivariate analysis of the different variables were performed according to the Cox's regression method. The variables of prognosis value, in decreasing order of importance, were: the decade of patient's admission, the modality of therapy employed, the presence of residual tumor in the surgical specimens and the clinical stage of the disease. Other variables like ethnic group, age of first menstrual flux, menopause, number of pregnancy, kind of delivery, number and kind of abortion, were found to be of no prognostic importance. The decade of admission was of independent prognostic significance. The presence of residual tumor in the surgical specimens was more important than lymph nodes spreading, but the overall survival was affected by the increase in the number of positive lymph nodes. Patient's age was a weak prognostic factor accounting for a reduction in the survival time among cases with age upper to 45 years old. Radiation therapy sterilizes a considerable number of lymph nodes but not all of them in every patient. There are a specific group of patients where the radical surgery is necessary in order to carry a complete debulking of the disease. (author)

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

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Carlos E. Galván-Tejada

2017-02-01

Full Text Available Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

Science.gov (United States)

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate statistical methods a first course

CERN Document Server

Marcoulides, George A

2014-01-01

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

An Introduction to Applied Multivariate Analysis

CERN Document Server

Raykov, Tenko

2008-01-01

Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions.

Science.gov (United States)

Bathke, Arne C; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-03-22

To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.

Expression of nerve growth factor and heme oxygenase-1 predict poor survival of breast carcinoma patients

International Nuclear Information System (INIS)

Noh, Sang Jae; Chung, Myoung Ja; Moon, Woo Sung; Kang, Myoung Jae; Jang, Kyu Yun; Bae, Jun Sang; Jamiyandorj, Urangoo; Park, Ho Sung; Kwon, Keun Sang; Jung, Sung Hoo; Youn, Hyun Jo; Lee, Ho; Park, Byung-Hyun

2013-01-01

Nerve growth factor (NGF) is a neurotrophin and has been suggested to induce heme oxygenase-1 (HO1) expression. Although the role of HO1 in tumorigenesis remains controversial, recent evidence suggests NGF and HO1 as tumor-progressing factors. However, the correlative role of NGF and HO1 and their prognostic impact in breast carcinoma is unknown. We investigated the expression and prognostic significance of the expression of NGF and HO1 in 145 cases of breast carcinoma. Immunohistochemical expression of NGF and HO1 was observed in 31% and 49% of breast carcinoma, respectively. The expression of NGF and HO1 significantly associated with each other, and both have a significant association with histologic grade, HER2 expression, and latent distant metastasis. The expression of NGF and HO1 predicted shorter overall survival of breast carcinoma by univariate and multivariate analysis. NGF expression was an independent prognostic indicator for relapse-free survival by multivariate analysis. The combined expression pattern of NGF and HO1 was also an independent prognostic indicator of overall survival and relapse-free survival. The patients with tumors expressing NGF had the shortest survival and the patients with tumor, which did not express NGF or HO1 showed the longest survival time. This study has demonstrated that individual expression of NGF or HO1, and the combined NGF/HO1 expression pattern could be prognostic indicators for breast carcinoma patients

Intrahepatic cholangiocarcinoma: impact of genetic hemochromatosis on outcome and overall survival after surgical resection.

Science.gov (United States)

Sulpice, Laurent; Rayar, Michel; Boucher, Eveline; Pele, Fabienne; Pracht, Marc; Meunier, Bernard; Boudjema, Karim

2013-03-01

The influence of genetic hemochromatosis (GH) on outcomes following surgical resections for intrahepatic cholangiocarcinoma (ICC) has not been evaluated. All patients with ICC who underwent a surgical resection between January 1997 and August 2011 were analyzed retrospectively. Risk factors were assessed by univariate and multivariate analyses. Eighty-seven patients were analyzed; 16 of these patients (18.4%) had GH. Among the 71 non-GH patients, 52 (73.2%) and 19 (26.8%) had normal or cirrhotic parenchyma, respectively. There was no significant difference in survival between the GH and non-GH patients. A univariate analysis showed that major hepatectomy (P = 0.012), intraoperative blood transfusion (P = 0.007), tumor size >5 cm (P = 0.006), several nodules (P < 0.001), and microvascular invasion (P = 0.04) were significantly associated with poor survival. A multivariate analysis showed that intraoperative blood infusion (HR 0.37; CI 95% [0.19; 0.71]) and more than one nodule (HR 2.5; CI 95% [1.06; 5.8]) were associated with a lower survival rate. Although the incidence of GH was high in our series, the presence of GH did not affect the outcomes after a liver hepatectomy for ICC. GH does not appear to increase recurrences or worsen the overall and disease-free survival. Copyright © 2013 Elsevier Inc. All rights reserved.

Resent state and multivariate analysis of a few juniper forests of baluchistan, pakistan

International Nuclear Information System (INIS)

Ahmed, M.; Siddiqui, M.F.

2015-01-01

Quantitative multivariate investigations were carried out to explore various forms of Juniper trees resulting human disturbances and natural phenomenon. Thirty stands were sampled by point centered quarter method and data were analysed using Wards cluster analysis and Bray-Curtis ordination. On the basis of multivariate analysis eight various forms i.e. healthy, unhealthy, over mature, disturbed, dieback, standing dead, logs and cut stem were recognized. Structural attributes were computed. Highest numbers (130-133 stem ha-1) of logs were recorded from Cautair and Khunk forests. Highest density ha-1 (229 ha-1) of healthy plants was estimated from Tangi Top area while lowest number (24 ha-1) of healthy plants was found from Saraghara area. Multivariate analysis showed five groups in cluster and ordination diagrams. These groups are characterized on the basis of healthy, over mature, disturbed and logged trees of Juniper. Higher number (115, 96, 84, 80 ha-1) of disturbed trees were distributed at Speena Sukher, Srag Kazi, Prang Shella and Tangi Top respectively. Overall density does not show any significant relation with basal area m2 ha-1, degree of slopes and the elevation of the sampling stands. Present study show that each and every Juniper stands are highly disturbed mostly due to human influence, therefore prompt conservational steps should be taken to safe these forests. (author)

Impact of socioeconomic status on survival of colorectal cancer patients.

Science.gov (United States)

Zhang, Qian; Wang, Yufu; Hu, Hanqing; Huang, Rui; Xie, Lei; Liu, Enrui; Chen, Ying-Gang; Wang, Guiyu; Wang, Xishan

2017-12-01

Socioeconomic status (SES) has an impact on the survival of various cancers, but it has not been fully understood in colorectal cancer (CRC). The Surveillance, Epidemiology and End Results database was adopted to detect the role of SES in the survival outcomes of CRC. A total of 184,322 eligible patients were included and SES status was analyzed. The multivariable analysis showed that Non-Hispanic Black (HR, 1.20; 95% CI, 1.15-1.24), being widowed (HR, 1.04; 95% CI, 1.01-1.07), any Medicaid (HR, 1.36; 95% CI, 1.33-1.39) and the lowest education level group patients had relative poorer prognosis. Besides, sex, tumor location, age, differentiation level and American Joint Committee on Cancer stage also had significant effects on overall survival of CRC. The individuals were further divided into five groups according to the number of survival-adverse factors. All of the four groups containing adverse factors showed impaired survival outcomes compared with the group containing no adverse factor.

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.

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

Enhancing tumor apparent diffusion coefficient histogram skewness stratifies the postoperative survival in recurrent glioblastoma multiforme patients undergoing salvage surgery.

Science.gov (United States)

Zolal, Amir; Juratli, Tareq A; Linn, Jennifer; Podlesek, Dino; Sitoci Ficici, Kerim Hakan; Kitzler, Hagen H; Schackert, Gabriele; Sobottka, Stephan B; Rieger, Bernhard; Krex, Dietmar

2016-05-01

Objective To determine the value of apparent diffusion coefficient (ADC) histogram parameters for the prediction of individual survival in patients undergoing surgery for recurrent glioblastoma (GBM) in a retrospective cohort study. Methods Thirty-one patients who underwent surgery for first recurrence of a known GBM between 2008 and 2012 were included. The following parameters were collected: age, sex, enhancing tumor size, mean ADC, median ADC, ADC skewness, ADC kurtosis and fifth percentile of the ADC histogram, initial progression free survival (PFS), extent of second resection and further adjuvant treatment. The association of these parameters with survival and PFS after second surgery was analyzed using log-rank test and Cox regression. Results Using log-rank test, ADC histogram skewness of the enhancing tumor was significantly associated with both survival (p = 0.001) and PFS after second surgery (p = 0.005). Further parameters associated with prolonged survival after second surgery were: gross total resection at second surgery (p = 0.026), tumor size (0.040) and third surgery (p = 0.003). In the multivariate Cox analysis, ADC histogram skewness was shown to be an independent prognostic factor for survival after second surgery. Conclusion ADC histogram skewness of the enhancing lesion, enhancing lesion size, third surgery, as well as gross total resection have been shown to be associated with survival following the second surgery. ADC histogram skewness was an independent prognostic factor for survival in the multivariate analysis.

Survival analysis of factors affecting incidence risk of Salmonella Dublin in Danish dairy herds during a 7-year surveillance period

DEFF Research Database (Denmark)

Nielsen, Liza Rosenbaum; Dohoo, Ian

2012-01-01

, proportional hazard model allowing for recurrence within herds. During October to December the hazard of failures was higher (hazard ratio HR=3.4, P=0.0005) than the rest of the year. Accounting for the delay in bulk-tank milk antibody responses to S. Dublin infection, this indicates that introduction......-quarters (YQs), either at the start of the study period or after recovery from infection. Survival analysis was performed on a dataset including 6931 dairy herds with 118969 YQs at risk, in which 1523 failures (new infection events) occurred. Predictors obtained from register data were tested in a multivariable...

SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

Directory of Open Access Journals (Sweden)

Masoud Asgharian

2010-12-01

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

Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

Science.gov (United States)

Rajagopalan, Vidya; Schwartzman, Armin; Hua, Xue; Leow, Alex; Thompson, Paul; Lepore, Natasha

2015-01-01

We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

A robust multivariate long run analysis of European electricity prices

OpenAIRE

Bruno Bosco; Lucia Parisio; Matteo Pelagatti; Fabio Baldi

2007-01-01

This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electrici...

Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach

DEFF Research Database (Denmark)

Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale

2011-01-01

deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... showed that it was possible to differentiate between the different batches depending on their history and that some batches were more oxidised than others. Furthermore, based on the results from the data analysis, critical control points in the entire production chain will be identified and strategies...

Authentication of Trappist beers by LC-MS fingerprints and multivariate data analysis.

Science.gov (United States)

Mattarucchi, Elia; Stocchero, Matteo; Moreno-Rojas, José Manuel; Giordano, Giuseppe; Reniero, Fabiano; Guillou, Claude

2010-12-08

The aim of this study was to asses the applicability of LC-MS profiling to authenticate a selected Trappist beer as part of a program on traceability funded by the European Commission. A total of 232 beers were fingerprinted and classified through multivariate data analysis. The selected beer was clearly distinguished from beers of different brands, while only 3 samples (3.5% of the test set) were wrongly classified when compared with other types of beer of the same Trappist brewery. The fingerprints were further analyzed to extract the most discriminating variables, which proved to be sufficient for classification, even using a simplified unsupervised model. This reduced fingerprint allowed us to study the influence of batch-to-batch variability on the classification model. Our results can easily be applied to different matrices and they confirmed the effectiveness of LC-MS profiling in combination with multivariate data analysis for the characterization of food products.

Marital status, treatment, and survival in patients with glioblastoma multiforme: a population based study.

Science.gov (United States)

Chang, Susan M; Barker, Fred G

2005-11-01

Social factors influence cancer treatment choices, potentially affecting patient survival. In the current study, the authors studied the interrelations between marital status, treatment received, and survival in patients with glioblastoma multiforme (GM), using population-based data. The data source was the Surveillance, Epidemiology, and End Results (SEER) Public Use Database, 1988-2001, 2004 release, all registries. Multivariate logistic, ordinal, and Cox regression analyses adjusted for demographic and clinical variables were used. Of 10,987 patients with GM, 67% were married, 31% were unmarried, and 2% were of unknown marital status. Tumors were slightly larger at the time of diagnosis in unmarried patients (49% of unmarried patients had tumors larger than 45 mm vs. 45% of married patients; P = 0.004, multivariate analysis). Unmarried patients were less likely to undergo surgical resection (vs. biopsy; 75% of unmarried patients vs. 78% of married patients) and were less likely to receive postoperative radiation therapy (RT) (70% of unmarried patients vs. 79% of married patients). On multivariate analysis, the odds ratio (OR) for resection (vs. biopsy) in unmarried patients was 0.88 (95% confidence interval [95% CI], 0.79-0.98; P = 0.02), and the OR for RT in unmarried patients was 0.69 (95% CI, 0.62-0.77; P Unmarried patients more often refused both surgical resection and RT. Unmarried patients who underwent surgical resection and RT were found to have a shorter survival than similarly treated married patients (hazard ratio for unmarried patients, 1.10; P = 0.003). Unmarried patients with GM presented with larger tumors, were less likely to undergo both surgical resection and postoperative RT, and had a shorter survival after diagnosis when compared with married patients, even after adjustment for treatment and other prognostic factors. (c) 2005 American Cancer Society.

Multivariable nonlinear analysis of foreign exchange rates

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2003-05-01

We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

Directory of Open Access Journals (Sweden)

Shikha Awasthi

2017-06-01

Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

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

Science.gov (United States)

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

2012-02-01

The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

Prehospital helicopter transport and survival of patients with traumatic brain injury.

Science.gov (United States)

Bekelis, Kimon; Missios, Symeon; Mackenzie, Todd A

2015-03-01

To investigate the association of helicopter transport with survival of patients with traumatic brain injury (TBI), in comparison with ground emergency medical services (EMS). Helicopter utilization and its effect on the outcomes of TBI remain controversial. We performed a retrospective cohort study involving patients with TBI who were registered in the National Trauma Data Bank between 2009 and 2011. Regression techniques with propensity score matching were used to investigate the association of helicopter transport with survival of patients with TBI, in comparison with ground EMS. During the study period, there were 209,529 patients with TBI who were registered in the National Trauma Data Bank and met the inclusion criteria. Of these patients, 35,334 were transported via helicopters and 174,195 via ground EMS. For patients transported to level I trauma centers, 2797 deaths (12%) were recorded after helicopter transport and 8161 (7.8%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival [OR (odds ratio), 1.95; 95% confidence interval (CI), 1.81-2.10; absolute risk reduction (ARR), 6.37%]. This persisted after propensity score matching (OR, 1.88; 95% CI, 1.74-2.03; ARR, 5.93%). For patients transported to level II trauma centers, 1282 deaths (10.6%) were recorded after helicopter transport and 5097 (7.3%) after ground EMS. Multivariable logistic regression analysis demonstrated an association of helicopter transport with increased survival (OR, 1.81; 95% CI, 1.64-2.00; ARR 5.17%). This again persisted after propensity score matching (OR, 1.73; 95% CI, 1.55-1.94; ARR, 4.69). Helicopter transport of patients with TBI to level I and II trauma centers was associated with improved survival, in comparison with ground EMS.

Pre-treatment inflammatory indexes as predictors of survival and cetuximab efficacy in metastatic colorectal cancer patients with wild-type RAS.

Science.gov (United States)

Yang, Jing; Guo, Xinli; Wang, Manni; Ma, Xuelei; Ye, Xiaoyang; Lin, Panpan

2017-12-07

This study aims at evaluating the prognostic significance of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation indexes (SII) in metastatic colorectal cancer (mCRC) patients treated with cetuximab. Ninety-five patients receiving cetuximab for mCRC were categorized into the high or low NLR, PLR, LMR, and SII groups based on their median index values. Univariate and multivariate survival analysis were performed to identify the indexes' correlation with progression-free survival (PFS) and overall survival (OS). In the univariate analysis, ECOG performance status, neutrphil counts, lymphocyte counts, monocyte counts, NLR, PLR, and LDH were associated with survival. Multivariate analysis showed that ECOG performance status of 0 (hazard ratio [HR] 3.608, p < 0.001; HR 5.030, p < 0.001, respectively), high absolute neutrophil counts (HR 2.837, p < 0.001; HR 1.922, p = 0.026, respectively), low lymphocyte counts (HR 0.352, p < 0.001; HR 0.440, p = 0.001, respectively), elevated NLR (HR 3.837, p < 0.001; HR 2.467, p = 0.006) were independent predictors of shorter PFS and OS. In conclusion, pre-treatment inflammatory indexes, especially NLR were potential biomarkers to predict the survival of mCRC patients with cetuximab therapy.

Using Survival Analysis to Evaluate Medical Equipment Battery Life.

Science.gov (United States)

Kuhajda, David

2016-01-01

As hospital medical device managers obtain more data, opportunities exist for using the data to improve medical device management, enhance patient safety, and evaluate costs of decisions. As a demonstration of the ability to use data analytics, this article applies survival analysis statistical techniques to assist in making decisions on medical equipment maintenance. The analysis was performed on a large amount of data related to failures of an infusion pump manufacturer's lithium battery and two aftermarket replacement lithium batteries from one hospital facility. The survival analysis resulted in statistical evidence showing that one of the third-party batteries had a lower survival curve than the infusion pump manufacturer's battery. This lower survival curve translates to a shorter expected life before replacement is needed. The data suggested that to limit unexpected failures, replacing batteries at a two-year interval, rather than the current industry recommendation of three years, may be warranted. For less than $5,400 in additional annual cost, the risk of unexpected battery failures can be reduced from an estimated 28% to an estimated 7%.

Relationship Between Radiation Treatment Time and Overall Survival After Induction Chemotherapy for Locally Advanced Head-and-Neck Carcinoma: A Subset Analysis of TAX 324

International Nuclear Information System (INIS)

Sher, David J.; Posner, Marshall R.; Tishler, Roy B.; Sarlis, Nicholas J.; Haddad, Robert I.; Holupka, Edward J.; Devlin, Phillip M.

2011-01-01

Purpose: To analyze the relationship between overall survival (OS) and radiation treatment time (RTT) and overall treatment time (OTT) in a well-described sequential therapy paradigm for locally advanced head-and-neck carcinoma (LAHNC). Methods and Materials: TAX 324 is a Phase III study comparing TPF (docetaxel, cisplatin, and fluorouracil) with PF (cisplatin and fluorouracil) induction chemotherapy (IC) in LAHNC patients; both arms were followed by carboplatin-based chemoradiotherapy (CRT). Prospective radiotherapy quality assurance was performed. This analysis includes all patients who received three cycles of IC and a radiation dose of ≥ 70 Gy. Radiotherapy treatment time was analyzed as binary (≤ 8 weeks vs. longer) and continuous (number of days beyond 8 weeks) functions. The primary analysis assessed the relationship between RTT, OTT, and OS, and the secondary analysis explored the association between treatment times and locoregional recurrence (LRR). Results: A total of 333 (of 501) TAX 324 patients met the criteria for inclusion in this analysis. There were no significant differences between the treatment arms in baseline or treatment characteristics. On multivariable analysis, PF IC, World Health Organization performance status of 1, non-oropharynx site, T3/4 stage, N3 status, and prolonged RTT (hazard ratio 1.63, p = 0.006) were associated with significantly inferior survival. Performance status, T3/4 disease, and prolonged RTT (odds ratio 1.68, p = 0.047) were independently and negatively related to LRR on multivariable analysis, whereas PF was not. Overall treatment time was not independently associated with either OS or LRR. Conclusions: In this secondary analysis of the TAX 324 trial, TPF IC remains superior to PF IC after controlling for radiotherapy delivery time. Even with optimal IC and concurrent chemotherapy, a non-prolonged RTT is a crucial determinant of treatment success. Appropriate delivery of radiotherapy after IC remains essential

Relationship Between Radiation Treatment Time and Overall Survival After Induction Chemotherapy for Locally Advanced Head-and-Neck Carcinoma: A Subset Analysis of TAX 324

Energy Technology Data Exchange (ETDEWEB)

Sher, David J., E-mail: dsher@partners.org [Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, MA (United States); Posner, Marshall R. [Division of Hematology/Oncology, Mount Sinai School of Medicine, New York, NY (United States); Tishler, Roy B. [Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, MA (United States); Sarlis, Nicholas J. [Sanofi-Aventis US, Bridgewater, NJ (United States); Haddad, Robert I. [Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Brigham and Women' s Hospital, Boston, MA (United States); Holupka, Edward J. [Department of Radiation Oncology, Beth Israel Deaconess Medical Center, Boston, MA (Israel); Devlin, Phillip M. [Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, MA (United States)

2011-12-01

Purpose: To analyze the relationship between overall survival (OS) and radiation treatment time (RTT) and overall treatment time (OTT) in a well-described sequential therapy paradigm for locally advanced head-and-neck carcinoma (LAHNC). Methods and Materials: TAX 324 is a Phase III study comparing TPF (docetaxel, cisplatin, and fluorouracil) with PF (cisplatin and fluorouracil) induction chemotherapy (IC) in LAHNC patients; both arms were followed by carboplatin-based chemoradiotherapy (CRT). Prospective radiotherapy quality assurance was performed. This analysis includes all patients who received three cycles of IC and a radiation dose of {>=} 70 Gy. Radiotherapy treatment time was analyzed as binary ({<=} 8 weeks vs. longer) and continuous (number of days beyond 8 weeks) functions. The primary analysis assessed the relationship between RTT, OTT, and OS, and the secondary analysis explored the association between treatment times and locoregional recurrence (LRR). Results: A total of 333 (of 501) TAX 324 patients met the criteria for inclusion in this analysis. There were no significant differences between the treatment arms in baseline or treatment characteristics. On multivariable analysis, PF IC, World Health Organization performance status of 1, non-oropharynx site, T3/4 stage, N3 status, and prolonged RTT (hazard ratio 1.63, p = 0.006) were associated with significantly inferior survival. Performance status, T3/4 disease, and prolonged RTT (odds ratio 1.68, p = 0.047) were independently and negatively related to LRR on multivariable analysis, whereas PF was not. Overall treatment time was not independently associated with either OS or LRR. Conclusions: In this secondary analysis of the TAX 324 trial, TPF IC remains superior to PF IC after controlling for radiotherapy delivery time. Even with optimal IC and concurrent chemotherapy, a non-prolonged RTT is a crucial determinant of treatment success. Appropriate delivery of radiotherapy after IC remains essential

Relationship between radiation treatment time and overall survival after induction chemotherapy for locally advanced head-and-neck carcinoma: a subset analysis of TAX 324.

Science.gov (United States)

Sher, David J; Posner, Marshall R; Tishler, Roy B; Sarlis, Nicholas J; Haddad, Robert I; Holupka, Edward J; Devlin, Phillip M

2011-12-01

To analyze the relationship between overall survival (OS) and radiation treatment time (RTT) and overall treatment time (OTT) in a well-described sequential therapy paradigm for locally advanced head-and-neck carcinoma (LAHNC). TAX 324 is a Phase III study comparing TPF (docetaxel, cisplatin, and fluorouracil) with PF (cisplatin and fluorouracil) induction chemotherapy (IC) in LAHNC patients; both arms were followed by carboplatin-based chemoradiotherapy (CRT). Prospective radiotherapy quality assurance was performed. This analysis includes all patients who received three cycles of IC and a radiation dose of ≥70 Gy. Radiotherapy treatment time was analyzed as binary (≤8 weeks vs. longer) and continuous (number of days beyond 8 weeks) functions. The primary analysis assessed the relationship between RTT, OTT, and OS, and the secondary analysis explored the association between treatment times and locoregional recurrence (LRR). A total of 333 (of 501) TAX 324 patients met the criteria for inclusion in this analysis. There were no significant differences between the treatment arms in baseline or treatment characteristics. On multivariable analysis, PF IC, World Health Organization performance status of 1, non-oropharynx site, T3/4 stage, N3 status, and prolonged RTT (hazard ratio 1.63, p=0.006) were associated with significantly inferior survival. Performance status, T3/4 disease, and prolonged RTT (odds ratio 1.68, p=0.047) were independently and negatively related to LRR on multivariable analysis, whereas PF was not. Overall treatment time was not independently associated with either OS or LRR. In this secondary analysis of the TAX 324 trial, TPF IC remains superior to PF IC after controlling for radiotherapy delivery time. Even with optimal IC and concurrent chemotherapy, a non-prolonged RTT is a crucial determinant of treatment success. Appropriate delivery of radiotherapy after IC remains essential for optimizing OS in LAHNC. Copyright © 2011 Elsevier Inc

Chemotherapy-related leukopenia as a biomarker predicting survival outcomes in locally advanced cervical cancer.

Science.gov (United States)

Bogani, Giorgio; Sabatucci, Ilaria; Maltese, Giuseppa; Lecce, Francesca; Signorelli, Mauro; Martinelli, Fabio; Chiappa, Valentina; Indini, Alice; Leone Roberti Maggiore, Umberto; Borghi, Chiara; Fucà, Giovanni; Ditto, Antonino; Raspagliesi, Francesco; Lorusso, Domenica

2017-01-01

To investigate the impact of hematologic toxicity and leukopenia in locally advanced cervical cancer patients undergoing neoadjuvant chemotherapy (NACT). Data of consecutive patients undergoing platinum-based NACT followed by surgery were retrospectively searched in order to evaluate the impact of chemotherapy-related toxicity on survival outcomes. Toxicity was graded per the Common Terminology Criteria for Adverse Events (CTCAEv.4.03). Survival outcomes were evaluated using Kaplan-Meir and Cox hazard models. Overall, 126 patients were included. Among those, 94 (74.6%) patients experienced grade2+ hematologic toxicity; while, grade2+ non-hematologic toxicity occurred in 11 (8.7%) patients. After a median follow-up of 37.1 (inter-quartile range, 12-57.5) months, 21 (16.6%) patients experienced recurrence. Via multivariate analysis, no factor was independently associated with disease-free survival; while a trend toward worse prognosis was observed for patients experiencing grade2+ leukopenia at cycle-3 (HR:3.13 (95%CI: 0.94, 10.3); p=0.06). Similarly, grade2+ leukopenia (HR:9.98 (95%CI: 1.14, 86.6); p=0.03), lymph-node positivity (HR:14.6 (95%CI:1.0, 214.4); p=0.05) and vaginal involvement (HR:5.81 (95%CI:1.43, 23.6); p=0.01) impacted on overall survival, at multivariate analysis. Magnitude of leukopenia correlated with survival (p<0.001). Although, our data have to be confirmed by prospective investigations, the present study shows an association between the occurrence of leukopenia and survival outcomes. NACT-related immunosuppression might reduce the response against the tumor, thus promoting cancer progression. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modeling time-to-event (survival) data using classification tree analysis.

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Linden, Ariel; Yarnold, Paul R

2017-12-01

Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

Brachytherapy Is Associated With Improved Survival in Inoperable Stage I Endometrial Adenocarcinoma: A Population-Based Analysis

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Acharya, Sahaja; Perkins, Stephanie M.; DeWees, Todd; Fischer-Valuck, Benjamin W. [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States); Mutch, David G.; Powell, Matthew A. [Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, Missouri (United States); Schwarz, Julie K. [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States); Grigsby, Perry W., E-mail: pgrigsby@radonc.wustl.edu [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States)

2015-11-01

Purpose: To assess the use of brachytherapy (BT) with or without external beam radiation (EBRT) in inoperable stage I endometrial adenocarcinoma in the United States and to determine the effect of BT on overall survival (OS) and cause-specific survival (CSS). Methods and Materials: Data between 1998 and 2011 from the National Cancer Institute's Surveillance, Epidemiology and End Results database were analyzed. Coarsened exact matching was used to adjust for differences in age and grade between patients who received BT and those who did not. Prognostic factors affecting OS and CSS were evaluated using the Kaplan-Meier product-limit method and a Cox proportional hazards regression model. Results: A total of 460 patients with inoperable stage I endometrial adenocarcinoma treated with radiation therapy were identified. Radiation consisted of either EBRT (n=260) or BT with or without EBRT (n=200). The only factor associated with BT use was younger patient age (median age, 72 vs 76 years, P=.001). Patients who received BT had a higher 3-year OS (60% vs 47%, P<.001) and CSS (82% vs 74%, P=.032) compared with those who did not. On multivariate analysis, BT use was independently associated with an improved OS (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.52-0.87) and CSS (HR 0.61, 95% CI 0.39-0.93). When patients were matched on age, BT use remained significant on multivariate analysis for OS (HR 0.65, 95% CI 0.48-0.87) and CSS (HR 0.52, 95% CI 0.31-0.84). When matched on age and grade, BT remained independently associated with improved OS and CSS (OS HR 0.62, 95% CI 0.46-0.83; CSS HR 0.57, 95% CI 0.34-0.92). Conclusion: Brachytherapy is independently associated with improved OS and CSS. It should be considered as part of the treatment regimen for stage I inoperable endometrial cancer patients undergoing radiation.

Brachytherapy Is Associated With Improved Survival in Inoperable Stage I Endometrial Adenocarcinoma: A Population-Based Analysis

International Nuclear Information System (INIS)

Acharya, Sahaja; Perkins, Stephanie M.; DeWees, Todd; Fischer-Valuck, Benjamin W.; Mutch, David G.; Powell, Matthew A.; Schwarz, Julie K.; Grigsby, Perry W.

2015-01-01

Purpose: To assess the use of brachytherapy (BT) with or without external beam radiation (EBRT) in inoperable stage I endometrial adenocarcinoma in the United States and to determine the effect of BT on overall survival (OS) and cause-specific survival (CSS). Methods and Materials: Data between 1998 and 2011 from the National Cancer Institute's Surveillance, Epidemiology and End Results database were analyzed. Coarsened exact matching was used to adjust for differences in age and grade between patients who received BT and those who did not. Prognostic factors affecting OS and CSS were evaluated using the Kaplan-Meier product-limit method and a Cox proportional hazards regression model. Results: A total of 460 patients with inoperable stage I endometrial adenocarcinoma treated with radiation therapy were identified. Radiation consisted of either EBRT (n=260) or BT with or without EBRT (n=200). The only factor associated with BT use was younger patient age (median age, 72 vs 76 years, P=.001). Patients who received BT had a higher 3-year OS (60% vs 47%, P<.001) and CSS (82% vs 74%, P=.032) compared with those who did not. On multivariate analysis, BT use was independently associated with an improved OS (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.52-0.87) and CSS (HR 0.61, 95% CI 0.39-0.93). When patients were matched on age, BT use remained significant on multivariate analysis for OS (HR 0.65, 95% CI 0.48-0.87) and CSS (HR 0.52, 95% CI 0.31-0.84). When matched on age and grade, BT remained independently associated with improved OS and CSS (OS HR 0.62, 95% CI 0.46-0.83; CSS HR 0.57, 95% CI 0.34-0.92). Conclusion: Brachytherapy is independently associated with improved OS and CSS. It should be considered as part of the treatment regimen for stage I inoperable endometrial cancer patients undergoing radiation.

Spatial compression algorithm for the analysis of very large multivariate images

Science.gov (United States)

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

Analysis of survival data with dependent censoring copula-based approaches

CERN Document Server

Emura, Takeshi

2018-01-01

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients’ survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers’ understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.

Identification by random forest method of HLA class I amino acid substitutions associated with lower survival at day 100 in unrelated donor hematopoietic cell transplantation.

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Marino, S R; Lin, S; Maiers, M; Haagenson, M; Spellman, S; Klein, J P; Binkowski, T A; Lee, S J; van Besien, K

2012-02-01

The identification of important amino acid substitutions associated with low survival in hematopoietic cell transplantation (HCT) is hampered by the large number of observed substitutions compared with the small number of patients available for analysis. Random forest analysis is designed to address these limitations. We studied 2107 HCT recipients with good or intermediate risk hematological malignancies to identify HLA class I amino acid substitutions associated with reduced survival at day 100 post transplant. Random forest analysis and traditional univariate and multivariate analyses were used. Random forest analysis identified amino acid substitutions in 33 positions that were associated with reduced 100 day survival, including HLA-A 9, 43, 62, 63, 76, 77, 95, 97, 114, 116, 152, 156, 166 and 167; HLA-B 97, 109, 116 and 156; and HLA-C 6, 9, 11, 14, 21, 66, 77, 80, 95, 97, 99, 116, 156, 163 and 173. In all 13 had been previously reported by other investigators using classical biostatistical approaches. Using the same data set, traditional multivariate logistic regression identified only five amino acid substitutions associated with lower day 100 survival. Random forest analysis is a novel statistical methodology for analysis of HLA mismatching and outcome studies, capable of identifying important amino acid substitutions missed by other methods.

The number and microlocalization of tumor-associated immune cells are associated with patient's survival time in non-small cell lung cancer

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Dai, Fuqiang; Liu, Lunxu; Che, Guowei; Yu, Nanbin; Pu, Qiang; Zhang, Shangfu; Ma, Junliang; Ma, Lin; You, Zongbing

2010-01-01

Tumor microenvironment is composed of tumor cells, fibroblasts, endothelial cells, and infiltrating immune cells. Tumor-associated immune cells may inhibit or promote tumor growth and progression. This study was conducted to determine whether the number and microlocalization of macrophages, mature dendritic cells and cytotoxic T cells in non-small cell lung cancer are associated with patient's survival time. Ninety-nine patients with non-small cell lung cancer (NSCLC) were included in this retrospective study. Paraffin-embedded NSCLC specimens and their clinicopathological data including up to 8-year follow-up information were used. Immunohistochemical staining for CD68 (marker for macrophages), CD83 (marker for mature dendritic cells), and CD8 (marker for cytotoxic T cells) was performed and evaluated in a blinded fashion. The numbers of immune cells in tumor islets and stroma, tumor islets, or tumor stroma were counted under a microscope. Correlation of the cell numbers and patient's survival time was analyzed using the Statistical Package for the Social Sciences (version 13.0). The numbers of macrophages, mature dendritic cells and cytotoxic T cells were significantly more in the tumor stroma than in the tumor islets. The number of macrophages in the tumor islets was positively associated with patient's survival time, whereas the number of macrophages in the tumor stroma was negatively associated with patient's survival time in both univariate and multivariate analyses. The number of mature dendritic cells in the tumor islets and stroma, tumor islets only, or tumor stroma only was positively associated with patient's survival time in a univariate analysis but not in a multivariate analysis. The number of cytotoxic T cells in the tumor islets and stroma was positively associated with patient's survival time in a univariate analysis but not in a multivariate analysis. The number of cytotoxic T cells in the tumor islets only or stroma

Epidermal Growth Factor Receptor Is Related to Poor Survival in Glioblastomas: Single-Institution Experience

Science.gov (United States)

Choi, Youngmin; Lee, Hyung-Sik; Hur, Won-Joo; Sung, Ki-Han; Kim, Ki-Uk; Choi, Sun-Seob; Kim, Su-Jin; Kim, Dae-Cheol

2013-01-01

Purpose There are conflicting results surrounding the prognostic significance of epidermal growth factor receptor (EGFR) status in glioblastoma (GBM) patients. Accordingly, we attempted to assess the influence of EGFR expression on the survival of GBM patients receiving postoperative radiotherapy. Materials and Methods Thirty three GBM patients who had received surgery and postoperative radiotherapy at our institute, between March 1997 and February 2006, were included. The evaluation of EGFR expression with immunohistochemistry was available for 30 patients. Kaplan-Meier survival analysis and Cox regression were used for statistical analysis. Results EGFR was expressed in 23 patients (76.7%), and not expressed in seven (23.3%). Survival in EGFR expressing GBM patients was significantly less than that in non-expressing patients (median survival: 12.5 versus 17.5 months, p=0.013). Patients who received more than 60 Gy showed improved survival over those who received up to 60 Gy (median survival: 17.0 versus 9.0 months, p=0.000). Negative EGFR expression and a higher radiation dose were significantly correlated with improved survival on multivariate analysis. Survival rates showed no differences according to age, sex, and surgical extent. Conclusion The expression of EGFR demonstrated a significantly deleterious effect on the survival of GBM patients. Therefore, approaches targeting EGFR should be considered in potential treatment methods for GBM patients, in addition to current management strategies. PMID:23225805

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

Science.gov (United States)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

Is Human Papillomavirus Associated with Prostate Cancer Survival?

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Mariarosa Pascale

2013-01-01

Full Text Available The role of human papillomavirus (HPV in prostate carcinogenesis is highly controversial: some studies suggest a positive association between HPV infection and an increased risk of prostate cancer (PCa, whereas others do not reveal any correlation. In this study, we investigated the prognostic impact of HPV infection on survival in 150 primary PCa patients. One hundred twelve (74.67% patients had positive expression of HPV E7 protein, which was evaluated in tumour tissue by immunohistochemistry. DNA analysis on a subset of cases confirmed HPV infection and revealed the presence of genotype 16. In Kaplan-Meier analysis, HPV-positive cancer patients showed worse overall survival (OS (median 4.59 years compared to HPV-negative (median 8.24 years, P=0.0381. In multivariate analysis age (P<0.001, Gleason score (P<0.001, nuclear grading (P=0.002, and HPV status (P=0.034 were independent prognostic factors for OS. In our cohort, we observed high prevalence of HPV nuclear E7 oncoprotein and an association between HPV infection and PCa survival. In the debate about the oncogenic activity of HPV in PCa, our results further confirm the need for additional studies to clarify the possible role of HPV in prostate carcinogenesis.

Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

International Nuclear Information System (INIS)

Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

2004-01-01

The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

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

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Guyot Patricia

2012-02-01

Full Text Available Abstract Background The results of Randomized Controlled Trials (RCTs on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios with statistics based on repeated reconstructions by multiple observers. Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. Conclusion The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

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Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

PROGNOSTIC FACTORS OF SURVIVAL IN RENAL CANCER

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A. V. Seriogin

2014-08-01

Full Text Available The purpose of the study was to reveal the independent anatomic, histological, and clinical factors of cancer-specific survival in patients with renal-cell carcinoma (RCC. For this, the authors retrospectively analyzed their experience with radical surgical treatments in 73 RCC patients operated on at the Department of Urology and Surgical Andrology, Russian Medical Academy of Postgraduate Education, from January 1, 1999 to December 31, 2004; their outcomes have become known by the present time. There was a statistically significant correlation of cancer-specific survival with its parameters, such as pathological stage of a tumor, its maximum pathological size, differentiation grade, involvement of regional lymph nodes, venous tumor thrombosis, level of thrombocytosis, and degree of the clinical symptoms of the disease. Multivariate analysis of survival in RCC in relation to the prognostic factors could reveal odd ratios for the limit values of significant prognostic factors. The statistically significant prognostic values established in the present study, as well as the molecular factors the implication of which is being now investigated can become in future an effective addition to the TNM staging system to define indications for certain treatments and to predict survival in RCC  

PROGNOSTIC FACTORS OF SURVIVAL IN RENAL CANCER

Directory of Open Access Journals (Sweden)

A. V. Seriogin

2009-01-01

Full Text Available The purpose of the study was to reveal the independent anatomic, histological, and clinical factors of cancer-specific survival in patients with renal-cell carcinoma (RCC. For this, the authors retrospectively analyzed their experience with radical surgical treatments in 73 RCC patients operated on at the Department of Urology and Surgical Andrology, Russian Medical Academy of Postgraduate Education, from January 1, 1999 to December 31, 2004; their outcomes have become known by the present time. There was a statistically significant correlation of cancer-specific survival with its parameters, such as pathological stage of a tumor, its maximum pathological size, differentiation grade, involvement of regional lymph nodes, venous tumor thrombosis, level of thrombocytosis, and degree of the clinical symptoms of the disease. Multivariate analysis of survival in RCC in relation to the prognostic factors could reveal odd ratios for the limit values of significant prognostic factors. The statistically significant prognostic values established in the present study, as well as the molecular factors the implication of which is being now investigated can become in future an effective addition to the TNM staging system to define indications for certain treatments and to predict survival in RCC  

Reassessment of the relationship between M-protein decrement and survival in multiple myeloma.

Science.gov (United States)

Palmer, M; Belch, A; Hanson, J; Brox, L

1989-01-01

The relationship between percentage M-protein decrement and survival is assessed in 134 multiple myeloma patients. The correlation did not achieve statistical significance (P = 0.069). Multivariate analysis using the Cox proportional hazards model, including a number of previously recognised prognostic factors, showed only percentage M-protein decrement, creatinine and haemoglobin to be significantly correlated with survival. However, the R'-statistic for each of these variables was low, indicating that their prognostic power is weak. We conclude that neither the percentage M-protein decrement nor the response derived from it can be used as an accurate means of assessing the efficacy of treatment in myeloma. Mature survival data alone should be used for this purpose.

Reassessment of the relationship between M-protein decrement and survival in multiple myeloma.

Science.gov (United States)

Palmer, M.; Belch, A.; Hanson, J.; Brox, L.

1989-01-01

The relationship between percentage M-protein decrement and survival is assessed in 134 multiple myeloma patients. The correlation did not achieve statistical significance (P = 0.069). Multivariate analysis using the Cox proportional hazards model, including a number of previously recognised prognostic factors, showed only percentage M-protein decrement, creatinine and haemoglobin to be significantly correlated with survival. However, the R'-statistic for each of these variables was low, indicating that their prognostic power is weak. We conclude that neither the percentage M-protein decrement nor the response derived from it can be used as an accurate means of assessing the efficacy of treatment in myeloma. Mature survival data alone should be used for this purpose. PMID:2757916

A primer of multivariate statistics

CERN Document Server

Harris, Richard J

2014-01-01

Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

Androgen deprivation does predict bNED survival in unfavorable prostate cancer PTS treated with external beam radiation therapy

International Nuclear Information System (INIS)

Anderson, Penny R.; Hanlon, Alexandra L.; Hanks, Gerald E.

1996-01-01

therapy. Figure 1 presents a comparison of bNED survival according to treatment using the 214 matched case/controls and Kaplan-Meier methodology. It supports the finding that hormonal therapy translates to improved bNED survival (at 5 yrs, 55% vs 37%, p=.0001) although there is not survival advantage. Multivariate analysis demonstrates that hormonal treatment is the most significant independent predictor of bNED survival (p 15) benefit from adjuvant hormonal therapy. 2) Upon multivariate analysis, hormonal therapy is determined to be the most significant predictor of bNED survival, followed by prerx PSA and palpation stage. 3) The 5-yr bNED survival rates of 55% for RT+H vs 37% for RT alone clearly demonstrate that those pts do benefit from adjuvant hormone therapy. 4) The bNED survival curves are separated by about 20 mos, representing a delay in disease progression with adjuvant hormonal therapy

Survival benefit of postoperative radiation in papillary meningioma: Analysis of the National Cancer Data Base.

Science.gov (United States)

Sumner, Whitney A; Amini, Arya; Hankinson, Todd C; Foreman, Nicholas K; Gaspar, Laurie E; Kavanagh, Brian D; Karam, Sana D; Rusthoven, Chad G; Liu, Arthur K

2017-01-01

Papillary meningioma represents a rare subset of World Health Organization (WHO) Grade III meningioma that portends an overall poor prognosis. There is relatively limited data regarding the benefit of postoperative radiation therapy (PORT). We used the National Cancer Data Base (NCDB) to compare overall survival (OS) outcomes of surgically resected papillary meningioma cases undergoing PORT compared to post-operative observation. The NCDB was queried for patients with papillary meningioma, diagnosed between 2004 and 2013, who underwent upfront surgery with or without PORT. Overall survival (OS) was determined using the Kaplan-Meier method. Univariate (UVA) and multivariate (MVA) analyses were performed. In total, 190 patients were identified; 89 patients underwent PORT, 101 patients were observed. Eleven patients received chemotherapy (6 with PORT, 5 without). 2-Year OS was significantly improved with PORT vs. no PORT (93.0% vs. 74.4%), as was 5-year OS (78.5% vs. 62.5%) (hazard ratio [HR], 0.48; 95% confidence interval [CI], 0.27-0.85; p  = 0.01). On MVA, patients receiving PORT had improved OS compared to observation (HR, 0.41; 95% CI, 0.22-0.76; p  = 0.005). On subset analysis by age group, the benefit of PORT vs. no PORT was significant in patients ≤18 years ( n  = 13), with 2-year OS of 85.7% vs. 50.0% (HR, 0.08; 95% CI, 0.01-0.80; p  = 0.032) and for patients >18 years ( n  = 184), with 2-year OS of 94.7% vs. 76.1% (HR, 0.55; 95% CI, 0.31-1.00; p  = 0.049), respectively. In this large contemporary analysis, PORT was associated with improved survival for both adult and pediatric patients with papillary meningioma. PORT should be considered in those who present with this rare, aggressive tumor.

Factors predicting survival in amyotrophic lateral sclerosis patients on non-invasive ventilation.

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Gonzalez Calzada, Nuria; Prats Soro, Enric; Mateu Gomez, Lluis; Giro Bulta, Esther; Cordoba Izquierdo, Ana; Povedano Panades, Monica; Dorca Sargatal, Jordi; Farrero Muñoz, Eva

2016-01-01

Non invasive ventilation (NIV) improves quality of life and extends survival in amyotrophic lateral sclerosis (ALS) patients. However, few data exist about the factors related to survival. We intended to assess the predictive factors that influence survival in patients after NIV initiation. Patients who started NIV from 2000 to 2014 and were tolerant (compliance ≥ 4 hours) were included; demographic, disease related and respiratory variables at NIV initiation were analysed. Statistical analysis was performed using the Kaplan-Meier test and Cox proportional hazard models. 213 patients were included with median survival from NIV initiation of 13.5 months. In univariate analysis, the identified risk factors for mortality were severity of bulbar involvement (HR 2), Forced Vital Capacity (FVC) % (HR 0.99) and ALSFRS-R (HR 0.97). Multivariate analysis showed that bulbar involvement (HR 1.92) and ALSFRS-R (HR 0.97) were independent predictive factors of survival in patients on NIV. In our study, the two prognostic factors in ALS patients following NIV were the severity of bulbar involvement and ALSFRS-R at the time on NIV initiation. A better assessment of bulbar involvement, including evaluation of the upper airway, and a careful titration on NIV are necessary to optimize treatment efficacy.

An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2012-01-01

Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

Causes of death in long-term lung cancer survivors: a SEER database analysis.

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Abdel-Rahman, Omar

2017-07-01

Long-term (>5 years) lung cancer survivors represent a small but distinct subgroup of lung cancer patients and information about the causes of death of this subgroup is scarce. The Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was utilized to determine the causes of death of long-term survivors of lung cancer. Survival analysis was conducted using Kaplan-Meier analysis and multivariate analysis was conducted using a Cox proportional hazard model. Clinicopathological characteristics and survival outcomes were assessed for the whole cohort. A total of 78,701 lung cancer patients with >5 years survival were identified. This cohort included 54,488 patients surviving 5-10 years and 24,213 patients surviving >10 years. Among patients surviving 5-10 years, 21.8% were dead because of primary lung cancer, 10.2% were dead because of other cancers, 6.8% were dead because of cardiac disease and 5.3% were dead because of non-malignant pulmonary disease. Among patients surviving >10 years, 12% were dead because of primary lung cancer, 6% were dead because of other cancers, 6.9% were dead because of cardiac disease and 5.6% were dead because of non-malignant pulmonary disease. On multivariate analysis, factors associated with longer cardiac-disease-specific survival in multivariate analysis include younger age at diagnosis (p death from primary lung cancer is still significant among other causes of death even 20 years after diagnosis of lung cancer. Moreover, cardiac as well as non-malignant pulmonary causes contribute a considerable proportion of deaths in long-term lung cancer survivors.

Multivariate statistical analysis of wildfires in Portugal

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Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

2013-04-01

Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

Integrated environmental monitoring and multivariate data analysis-A case study.

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Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate

Nutritional factors as predictors of response to radio-chemotherapy and survival in unresectable squamous head and neck carcinoma

International Nuclear Information System (INIS)

Salas, Sebastien; Deville, Jean-Laurent; Giorgi, Roch; Pignon, Thierry; Bagarry, Danielle; Barrau, Karine; Zanaret, Michel; Giovanni, Antoine; Bourgeois, Aude; Favre, Roger; Duffaud, Florence

2008-01-01

Background and purpose: This study sought to evaluate nutritional prognostic factors before treatment in patients with unresectable head and neck cancer treated by concomitant radio-chemotherapy. Methods and materials: Seventy-two consecutive patients were treated. We studied the potential effects of CRP, Alb, preAlb, orosomucoid, weight, weight history, BMI, PINI, OPR and NRI on response to treatment, Event-Free Survival (EFS) and Overall Survival (OS). Effects of potential risk factors on OS and on EFS were analyzed by computing Kaplan-Meier estimates, and curves were compared using the log-rank test. Results: All biological nutritional factors were statistically correlated with the response to radio-chemotherapy. In multivariate analysis, only CRP (p = 0.004) remained statistically significant. A statistical correlation was found between Alb and EFS in multivariate analysis (p = 0.04). The factors influencing OS in univariate analysis were Alb (p = 0.008), CRP (p = 0.004), orosomucoid (p = 0.01) and NRI (p = 0.01), response to radio-chemotherapy (p < 0.001) and staging (p = 0.04). In multivariate analysis, only the response to radio-chemotherapy (p < 0.001) remained significant. Conclusions: This study illustrates the prognostic value of nutritional status. CRP and Alb may be useful in the assessment of advanced head and neck cancer patients at diagnosis and for stratifying patients taking part in randomized trials

Structural analysis and design of multivariable control systems: An algebraic approach

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Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

1988-01-01

The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

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Benoit Parmentier

2014-12-01

Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

Pediatric differentiated thyroid carcinoma in stage I: risk factor analysis for disease free survival

International Nuclear Information System (INIS)

Wada, Nobuyuki; Rino, Yasushi; Masuda, Munetaka; Ito, Koichi; Sugino, Kiminori; Mimura, Takashi; Nagahama, Mitsuji; Kitagawa, Wataru; Shibuya, Hiroshi; Ohkuwa, Keiko; Nakayama, Hirotaka; Hirakawa, Shohei

2009-01-01

To examine the outcomes and risk factors in pediatric differentiated thyroid carcinoma (DTC) patients who were defined as TNM stage I because some patients develop disease recurrence but treatment strategy for such stage I pediatric patients is still controversial. We reviewed 57 consecutive TNM stage I patients (15 years or less) with DTC (46 papillary and 11 follicular) who underwent initial treatment at Ito Hospital between 1962 and 2004 (7 males and 50 females; mean age: 13.1 years; mean follow-up: 17.4 years). Clinicopathological results were evaluated in all patients. Multivariate analysis was performed to reveal the risk factors for disease-free survival (DFS) in these 57 patients. Extrathyroid extension and clinical lymphadenopathy at diagnosis were found in 7 and 12 patients, respectively. Subtotal/total thyroidectomy was performed in 23 patients, modified neck dissection in 38, and radioactive iodine therapy in 10. Pathological node metastasis was confirmed in 37 patients (64.9%). Fifteen patients (26.3%) exhibited local recurrence and 3 of them also developed metachronous lung metastasis. Ten of these 15 achieved disease-free after further treatments and no patients died of disease. In multivariate analysis, male gender (p = 0.017), advanced tumor (T3, 4a) stage (p = 0.029), and clinical lymphadenopathy (p = 0.006) were risk factors for DFS in stage I pediatric patients. Male gender, tumor stage, and lymphadenopathy are risk factors for DFS in stage I pediatric DTC patients. Aggressive treatment (total thyroidectomy, node dissection, and RI therapy) is considered appropriate for patients with risk factors, whereas conservative or stepwise approach may be acceptable for other patients

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

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Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

2015-09-01

To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

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Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

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Jankulovska Mirjana

2014-01-01

Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

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D'Amico, E J; Neilands, T B; Zambarano, R

2001-11-01

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

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

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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

Survival analysis for customer satisfaction: A case study

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

2017-11-01

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

Impact of socioeconomic status on survival for patients with anal cancer.

Science.gov (United States)

Lin, Daniel; Gold, Heather T; Schreiber, David; Leichman, Lawrence P; Sherman, Scott E; Becker, Daniel J

2018-04-15

Although outcomes for patients with squamous cell carcinoma of the anus (SCCA) have improved, the gains in benefit may not be shared uniformly among patients of disparate socioeconomic status. In the current study, the authors investigated whether area-based median household income (MHI) is predictive of survival among patients with SCCA. Patients diagnosed with SCCA from 2004 through 2013 in the Surveillance, Epidemiology, and End Results registry were included. Socioeconomic status was defined by census-tract MHI level and divided into quintiles. Multivariable Cox proportional hazards models and logistic regression were used to study predictors of survival and radiotherapy receipt. A total of 9550 cases of SCCA were included. The median age of the patients was 58 years, 63% were female, 85% were white, and 38% were married. In multivariable analyses, patients living in areas with lower MHI were found to have worse overall survival and cancer-specific survival (CSS) compared with those in the highest income areas. Mortality hazard ratios for lowest to highest income were 1.32 (95% confidence interval [95% CI], 1.18-1.49), 1.31 (95% CI, 1.16-1.48), 1.19 (95% CI, 1.06-1.34), and 1.16 (95% CI, 1.03-1.30). The hazard ratios for CSS similarly ranged from 1.34 to 1.22 for lowest to highest income. Older age, black race, male sex, unmarried marital status, an earlier year of diagnosis, higher tumor grade, and later American Joint Committee on Cancer stage of disease also were associated with worse CSS. Income was not found to be associated with the odds of initiating radiotherapy in multivariable analysis (odds ratio of 0.87 for lowest to highest income level; 95% CI, 0.63-1.20). MHI appears to independently predict CSS and overall survival in patients with SCCA. Black race was found to remain a predictor of SCCA survival despite controlling for income. Further study is needed to understand the mechanisms by which socioeconomic inequalities affect cancer care and

Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

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Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

2013-06-01

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

Science.gov (United States)

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

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Philip A Kragel

2012-11-01

Full Text Available Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA of functional magnetic resonance imaging (fMRI data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that nonlinear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

Survival in patients with oral and maxillofacial diffuse large B-cell lymphoma

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Janet Ofelia Guevara-Canales

2013-11-01

Full Text Available The purpose of this study was to determine the survival and prognostic factors of patients with diffuse large B-cell lymphoma (DLBCL of the oral cavity and maxillofacial region. Retrospectively, the clinical records of patients with a primary diagnosis of DLBCL of the oral cavity and maxillofacial region treated at the A.C. Camargo Hospital for Cancer, São Paulo, Brazil, between January 1980 and December 2005 were evaluated to determine (A overall survival (OS at 2 and 5 years and the individual survival percentage for each possible prognostic factor by means of the actuarial technique (also known as mortality tables, and the Kaplan Meier product limit method (which provided the survival value curves for each possible prognostic factor; (B prognostic factors subject to univariate evaluation with the log-rank test (also known as Mantel-Cox, and multivariate analysis with Cox's regression model (all the variables together. The data were considered significant at p ≤ 0.05. From 1980 to 2005, 3513 new cases of lymphomas were treated, of which 151 (4.3% occurred in the oral cavity and maxillofacial region. Of these 151 lesions, 48 were diffuse large B-cell lymphoma, with 64% for OS at 2 years and 45% for OS at 5 years. Of the variables studied as possible prognostic factors, multivariate analysis found the following variables have statistically significant values: age (p = 0.042, clinical stage (p = 0.007 and performance status (p = 0.031. These data suggest that patients have a higher risk of mortality if they are older, at a later clinical stage, and have a higher performance status.

Radiation therapy improves survival in rectal small cell cancer - Analysis of Surveillance Epidemiology and End Results (SEER) data.

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Modrek, Aram S; Hsu, Howard C; Leichman, Cynthia G; Du, Kevin L

2015-04-24

Small cell carcinoma of the rectum is a rare neoplasm with scant literature to guide treatment. We used the Surveillance Epidemiology and End Results (SEER) database to investigate the role of radiation therapy in the treatment of this cancer. The SEER database (National Cancer Institute) was queried for locoregional cases of small cell rectal cancer. Years of diagnosis were limited to 1988-2010 (most recent available) to reduce variability in staging criteria or longitudinal changes in surgery and radiation techniques. Two month conditional survival was applied to minimize bias by excluding patients who did not survive long enough to receive cancer-directed therapy. Patient demographics between the RT and No_RT groups were compared using Pearson Chi-Square tests. Overall survival was compared between patients who received radiotherapy (RT, n = 43) and those who did not (No_RT, n = 28) using the Kaplan-Meier method. Multivariate Cox proportional hazards model was used to evaluate important covariates. Median survival was significantly longer for patients who received radiation compared to those who were not treated with radiation; 26 mo vs. 8 mo, respectively (log-rank P = 0.009). We also noted a higher 1-year overall survival rate for those who received radiation (71.1% vs. 37.8%). Unadjusted hazard ratio for death (HR) was 0.495 with the use of radiation (95% CI 0.286-0.858). Among surgery, radiotherapy, sex and age at diagnosis, radiation therapy was the only significant factor for overall survival with a multivariate HR for death of 0.393 (95% CI 0.206-0.750, P = 0.005). Using SEER data, we have identified a significant survival advantage with the use of radiation therapy in the setting of rectal small cell carcinoma. Limitations of the SEER data apply to this study, particularly the lack of information on chemotherapy usage. Our findings strongly support the use of radiation therapy for patients with locoregional small cell rectal cancer.

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

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

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Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

Multifactorial risk assessment for survival of abutments of removable partial dentures based on practice-based longitudinal study.

Science.gov (United States)

Tada, Sayaka; Ikebe, Kazunori; Matsuda, Ken-Ichi; Maeda, Yoshinobu

2013-12-01

Predicting the tooth survival is such a great challenge for evidence-based dentistry. To prevent further tooth loss of partially edentulous patients, estimation of individualized risk and benefit for each residual tooth is important to the clinical decision-making. While there are several reports indicating a risk of losing the abutment teeth of RPDs, there are no existing reports exploring the cause of abutment loss by multifactorial analysis. The aim of this practice-based longitudinal study was to determine the prognostic factors affecting the survival period of RPD abutments using a multifactorial risk assessment. One hundred and forty-seven patients had been previously provided with a total of 236 new RPDs at the Osaka University Dental Hospital; the 856 abutments for these RPDs were analyzed. Survival of abutment teeth was estimated using the Kaplan-Meier method. Multivariate analysis was conducted by Cox's proportional hazard modelling. The 5-year survival rates were 86.6% for direct abutments and 93.1% for indirect abutments, compared with 95.8% survival in non-abutment teeth. The multivariate analysis showed that abutment survival was significantly associated with crown-root ratio (hazard ratio (HR): 3.13), root canal treatment (HR: 2.93), pocket depth (HR: 2.51), type of abutments (HR: 2.19) and occlusal support (HR: 1.90). From this practice-based longitudinal study, we concluded that RPD abutment teeth are more likely to be lost than other residual teeth. From the multifactorial risk factor assessment, several prognostic factors, such as occlusal support, crown-root ratio, root canal treatment, and pocket depth were suggested. These results could be used to estimate the individualized risk for the residual teeth, to predict the prognosis of RPD abutments and to facilitate an evidence-based clinical decision making. Copyright © 2013 Elsevier Ltd. All rights reserved.

Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

DEFF Research Database (Denmark)

Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

2005-01-01

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable...... data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later....

Survival outcomes for oligometastasis in resected non-small cell lung cancer.

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Shimada, Yoshihisa; Saji, Hisashi; Kakihana, Masatoshi; Kajiwara, Naohiro; Ohira, Tatsuo; Ikeda, Norihiko

2015-10-01

We investigated the factors associated with post-recurrence survival and the treatment for non-small-cell lung cancer patients with postoperative distant recurrence, especially oligometastasis. We reviewed the data of 272 patients with distant recurrence who underwent resection of non-small-cell lung cancer from January 2000 through December 2011. The type of distant recurrence was classified as oligometastasis (n = 76, 28%) or polymetastasis (n = 196, 72%). Forty-seven (62%) patients with oligometastasis received local therapy (surgery 5, radiotherapy 9, sequential local and systemic therapy 28, chemoradiotherapy 5). Multivariate analysis revealed older age, non-adenocarcinoma, shorter disease-free interval, no pulmonary metastasis, liver metastases, bone metastases, and polymetastasis had significant associations with unfavorable post-recurrence survival. Subgroup analysis of patients with oligometastasis showed histology and disease-free interval had a great impact on survival. Smoking history and histology were associated with survival in patients with lung oligometastasis, whereas systemic treatment and longer disease-free interval were related to increased post-recurrence survival in those with brain oligometastasis. This study showed that an oligometastatic state per se was a significant favorable factor. Optimization of personalized systemic treatment and adding local treatment are important in the management of patients with non-small-cell lung cancer and oligometastasis. © The Author(s) 2015.

The influence of sarcopenia on survival and surgical complications in ovarian cancer patients undergoing primary debulking surgery.

Science.gov (United States)

Rutten, I J G; Ubachs, J; Kruitwagen, R F P M; van Dijk, D P J; Beets-Tan, R G H; Massuger, L F A G; Olde Damink, S W M; Van Gorp, T

2017-04-01

Sarcopenia, severe skeletal muscle loss, has been identified as a prognostic factor in various malignancies. This study aims to investigate whether sarcopenia is associated with overall survival (OS) and surgical complications in patients with advanced ovarian cancer undergoing primary debulking surgery (PDS). Ovarian cancer patients (n = 216) treated with PDS were enrolled retrospectively. Total skeletal muscle surface area was measured on axial computed tomography at the level of the third lumbar vertebra. Optimum stratification was used to find the optimal skeletal muscle index cut-off to define sarcopenia (≤38.73 cm 2 /m 2 ). Cox-regression and Kaplan-Meier analysis were used to analyse the relationship between sarcopenia and OS. The effect of sarcopenia on the development of major surgical complications was studied with logistic regression. Kaplan-Meier analysis showed a significant survival disadvantage for patients with sarcopenia compared to patients without sarcopenia (p = 0.010). Sarcopenia univariably predicted OS (HR 1.536 (95% CI 1.105-2.134), p = 0.011) but was not significant in multivariable Cox-regression analysis (HR 1.362 (95% CI 0.968-1.916), p = 0.076). Significant predictors for OS in multivariable Cox-regression analysis were complete PDS, treatment in a specialised centre and the development of major complications. Sarcopenia was not predictive of major complications. Sarcopenia was not predictive of OS or major complications in ovarian cancer patients undergoing primary debulking surgery. However a strong trend towards a survival disadvantage for patients with sarcopenia was seen. Future prospective studies should focus on interventions to prevent or reverse sarcopenia and possibly increase ovarian cancer survival. Complete cytoreduction remains the strongest predictor of ovarian cancer survival. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights

Lymph Node Micrometastases are Associated with Worse Survival in Patients with Otherwise Node-Negative Hilar Cholangiocarcinoma.

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Mantel, Hendrik T J; Wiggers, Jim K; Verheij, Joanne; Doff, Jan J; Sieders, Egbert; van Gulik, Thomas M; Gouw, Annette S H; Porte, Robert J

2015-12-01

Lymph node metastases on routine histology are a strong negative predictor for survival after resection of hilar cholangiocarcinoma. Additional immunohistochemistry can detect lymph node micrometastases in patients who are otherwise node negative, but the prognostic value is unsure. The objective of this study was to assess the effect on survival of immunohistochemically detected lymph node micrometastases in patients with node-negative (pN0) hilar cholangiocarcinoma on routine histology. Between 1990 and 2010, a total of 146 patients underwent curative-intent resection of hilar cholangiocarcinoma with regional lymphadenectomy at two university medical centers in the Netherlands. Ninety-one patients (62 %) without lymph node metastases at routine histology were included. Micrometastases were identified by multiple sectioning of all lymph nodes and additional immunostaining with an antibody against cytokeratin 19 (K19). The association with overall survival was assessed in univariable and multivariable analysis. Median follow-up was 48 months. Micrometastases were identified in 16 (5 %) of 324 lymph nodes, corresponding to 11 (12 %) of 91 patients. There were no differences in clinical variables between K19 lymph node-positive and -negative patients. Five-year survival rates in patients with lymph node micrometastases were significantly lower compared to patients without micrometastases (27 vs. 54 %, P = 0.01). Multivariable analysis confirmed micrometastases as an independent prognostic factor for survival (adjusted Hazard ratio 2.4, P = 0.02). Lymph node micrometastases are associated with worse survival after resection of hilar cholangiocarcinoma. Immunohistochemical detection of lymph node micrometastases leads to better staging of patients who were initially diagnosed with node-negative (pN0) hilar cholangiocarcinoma on routine histology.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

Science.gov (United States)

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

Multivariate missing data in hydrology - Review and applications

Science.gov (United States)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

Science.gov (United States)

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Racial differences in treatment and survival in older patients with diffuse large B-cell lymphoma (DLBCL)

International Nuclear Information System (INIS)

Griffiths, Robert; Gleeson, Michelle; Knopf, Kevin; Danese, Mark

2010-01-01

Diffuse large B-cell lymphoma (DLBCL) comprises 31% of lymphomas in the United States. Although it is an aggressive type of lymphoma, 40% to 50% of patients are cured with treatment. The study objectives were to identify patient factors associated with treatment and survival in DLBCL. Using Surveillance, Epidemiology, and End Results (SEER) registry data linked to Medicare claims, we identified 7,048 patients diagnosed with DLBCL between January 1, 2001 and December 31, 2005. Patients were followed from diagnosis until the end of their claims history (maximum December 31, 2007) or death. Medicare claims were used to characterize the first infused chemo-immunotherapy (C-I therapy) regimen and to identify radiation. Multivariate analyses were performed to identify patient demographic, socioeconomic, and clinical factors associated with treatment and with survival. Outcomes variables in the survival analysis were all-cause mortality, non-Hodgkin's lymphoma (NHL) mortality, and other/unknown cause mortality. Overall, 84% (n = 5,887) received C-I therapy or radiation treatment during the observation period: both, 26%; C-I therapy alone, 53%; and radiation alone, 5%. Median age at diagnosis was 77 years, 54% were female, 88% were white, and 43% had Stage III or IV disease at diagnosis. The median time to first treatment was 42 days, and 92% of these patients had received their first treatment by day 180 following diagnosis. In multivariate analysis, the treatment rate was significantly lower among patients ≥ 80 years old, blacks versus whites, those living in a census tract with ≥ 12% poverty, and extra-nodal disease. Blacks had a lower treatment rate overall (Hazard Ratio [HR] 0.77; P < 0.001), and were less likely to receive treatment within 180 days of diagnosis (Odds Ratio [OR] 0.63; P = 0.002) than whites. In multivariate survival analysis, black race was associated with higher all-cause mortality (HR 1.24; P = 0.01) and other/unknown cause mortality (HR 1

Relation between delay and survival in 596 patients with breast cancer.

Science.gov (United States)

Machiavelli, M; Leone, B; Romero, A; Perez, J; Vallejo, C; Bianco, A; Rodriguez, R; Estevez, R; Chacon, R; Dansky, C

1989-01-01

To evaluate the influence of delay between first symptom and first treatment upon survival the medical records of 596 patients with breast cancer were reviewed. The following intervals were considered: less than 3 months; 3-6 months and greater than 6 months. Patients in the less than 3 months delay group had a better distribution by clinical stages and a 10-year survival rate higher than those in the longer delay groups (p = 0.034). However, within each stage no statistically significant difference in survival according to delay was observed. A Cox multivariate analysis revealed that performance status and stage of disease were independent predictors of survival, but not delay. Assuming the best prognosis for patients with clinical stages I and II and less than 3 months delay, the group with longer delay times had 15 deaths over what would have been predicted. This adverse effect was observed almost exclusively among patients over age 50 (14/15).

Impact of donor-recipient sex match on long-term survival after heart transplantation in children: An analysis of 5797 pediatric heart transplants.

Science.gov (United States)

Kemna, Mariska; Albers, Erin; Bradford, Miranda C; Law, Sabrina; Permut, Lester; McMullan, D Mike; Law, Yuk

2016-03-01

The effect of donor-recipient sex matching on long-term survival in pediatric heart transplantation is not well known. Adult data have shown worse survival when male recipients receive a sex-mismatched heart, with conflicting results in female recipients. We analyzed 5795 heart transplant recipients ≤ 18 yr in the Scientific Registry of Transplant Recipients (1990-2012). Recipients were stratified based on donor and recipient sex, creating four groups: MM (N = 1888), FM (N = 1384), FF (N = 1082), and MF (N = 1441). Males receiving sex-matched donor hearts had increased unadjusted allograft survival at five yr (73.2 vs. 71%, p = 0.01). However, this survival advantage disappeared with longer follow-up and when adjusted for additional risk factors by multivariable Cox regression analysis. In contrast, for females, receiving a sex-mismatched heart was associated with an 18% higher risk of allograft loss over time compared to receiving a sex-matched heart (HR 1.18, 95% CI: 1.00-1.38) and a 26% higher risk compared to sex-matched male recipients (HR 1.26, 95% CI: 1.10-1.45). Females who receive a heart from a male donor appear to have a distinct long-term survival disadvantage compared to all other groups. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Reduction of the dimensionality and comparative analysis of multivariate radiological data

International Nuclear Information System (INIS)

Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

2009-01-01

Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of 238 U, 226 Ra or 232 Th, combined with the concentration of 40 K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

A Novel and Effective Multivariate Method for Compositional Analysis using Laser Induced Breakdown Spectroscopy

International Nuclear Information System (INIS)

Wang, W; Qi, H; Ayhan, B; Kwan, C; Vance, S

2014-01-01

Compositional analysis is important to interrogate spectral samples for direct analysis of materials in agriculture, environment and archaeology, etc. In this paper, multi-variate analysis (MVA) techniques are coupled with laser induced breakdown spectroscopy (LIBS) to estimate quantitative elemental compositions and determine the type of the sample. In particular, we present a new multivariate analysis method for composition analysis, referred to as s pectral unmixing . The LIBS spectrum of a testing sample is considered as a linear mixture with more than one constituent signatures that correspond to various chemical elements. The signature library is derived from regression analysis using training samples or is manually set up with the information from an elemental LIBS spectral database. A calibration step is used to make all the signatures in library to be homogeneous with the testing sample so as to avoid inhomogeneous signatures that might be caused by different sampling conditions. To demonstrate the feasibility of the proposed method, we compare it with the traditional partial least squares (PLS) method and the univariate method using a standard soil data set with elemental concentration measured a priori. The experimental results show that the proposed method holds great potential for reliable and effective elemental concentration estimation

Multivariate factor analysis of Girgentana goat milk composition

Directory of Open Access Journals (Sweden)

Pietro Giaccone

2010-01-01

Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

Survival analysis in hematologic malignancies: recommendations for clinicians

Science.gov (United States)

Delgado, Julio; Pereira, Arturo; Villamor, Neus; López-Guillermo, Armando; Rozman, Ciril

2014-01-01

The widespread availability of statistical packages has undoubtedly helped hematologists worldwide in the analysis of their data, but has also led to the inappropriate use of statistical methods. In this article, we review some basic concepts of survival analysis and also make recommendations about how and when to perform each particular test using SPSS, Stata and R. In particular, we describe a simple way of defining cut-off points for continuous variables and the appropriate and inappropriate uses of the Kaplan-Meier method and Cox proportional hazard regression models. We also provide practical advice on how to check the proportional hazards assumption and briefly review the role of relative survival and multiple imputation. PMID:25176982

A multivariate analysis of Antarctic sea ice since 1979

Energy Technology Data Exchange (ETDEWEB)

Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)

2012-03-15

Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)

Survival after Abdominoperineal and Sphincter-Preserving Resection in Nonmetastatic Rectal Cancer: A Population-Based Time-Trend and Propensity Score-Matched SEER Analysis

Directory of Open Access Journals (Sweden)

Rene Warschkow

2017-01-01

Full Text Available Background. Abdominoperineal resection (APR has been associated with impaired survival in nonmetastatic rectal cancer patients. It is unclear whether this adverse outcome is due to the surgical procedure itself or is a consequence of tumor-related characteristics. Study Design. Patients were identified from the Surveillance, Epidemiology, and End Results database. The impact of APR compared to coloanal anastomosis (CAA on survival was assessed by Cox regression and propensity-score matching. Results. In 36,488 patients with rectal cancer resection, the APR rate declined from 31.8% in 1998 to 19.2% in 2011, with a significant trend change in 2004 at 21.6% (P<0.001. To minimize a potential time-trend bias, survival analysis was limited to patients diagnosed after 2004. APR was associated with an increased risk of cancer-specific mortality after unadjusted analysis (HR = 1.61, 95% CI: 1.28–2.03, P<0.01 and multivariable adjustment (HR = 1.39, 95% CI: 1.10–1.76, P<0.01. After optimal adjustment of highly biased patient characteristics by propensity-score matching, APR was not identified as a risk factor for cancer-specific mortality (HR = 0.85, 95% CI: 0.56–1.29, P=0.456. Conclusions. The current propensity score-adjusted analysis provides evidence that worse oncological outcomes in patients undergoing APR compared to CAA are caused by different patient characteristics and not by the surgical procedure itself.

Recipient Related Prognostic Factors for Graft Survival after Kidney Transplantation. A Single Center Experience

Directory of Open Access Journals (Sweden)

Alina Daciana ELEC

2012-09-01

Full Text Available Background and Aim. Advanced chronic kidney disease (CKD severely impairs life expectancy and quality of life in affected patients. Considering its benefits, renal transplantation currently represents the optimal treatment solution for end stage kidney disease patients. Pre-transplant assessment aims to maximize the graft and patient survival by identifying potential factors influencing the post-transplant outcome. The aim of this study has been to analyze recipient related prognostic factors bearing an impact on graft survival. Material and Methods. We analyzed the graft outcomes of 426 renal transplantations performed at the Clinical Institute of Urology and Renal Transplantation of Cluj-Napoca, between January 2004 and December 2008. Variables related to recipient and to potential donor/recipient prognostic factors were studied using univariate and multivariate analysis. Results. Graft survivals at 1, 3, 5 and 7 years were 94.01%, 88.37%, 82.51% and 78.10%, respectively. Chronic rejection (41.11% and death with a functioning graft (18.88% were the main causes of graft loss. In uni and multivariate analysis the recipient related variables found to influence the renal graft outcome were: peritoneal dialysis, pre transplant residual diuresis, grade I hypertension, severe iliac vessel atheromatosis, ischemic heart disease, stroke history, dyslipidemia and denutrition. The worst graft outcomes have been found for recipients on peritoneal dialysis, with anuria, hypotension, severe iliac atheromatosis, ischemic heart disease, stroke history, dyslipidemia and a poor nutritional status. Conclusion. The type of dialysis, the pre transplant residual diuresis, recipient arterial blood pressure, iliac vessel atheromatosis, ischemic heart disease, stroke history, dyslipidemia and denutrition significantly influence graft survival.

Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

OpenAIRE

Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

2017-01-01

Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

Factors associated with improved survival among older colorectal cancer patients in the US: a population-based analysis

Directory of Open Access Journals (Sweden)

Earle Craig C

2009-07-01

Full Text Available Abstract Background The purpose of this study was to estimate the relative impact of changes in demographics, stage at detection, treatment mix, and medical technology on 5-year survival among older colorectal cancer (CRC patients. Methods We selected older patients diagnosed with CRC between 1992 and 2000 from the SEER-Medicare database and followed them through 2005. Trends in demographic characteristics, stage at detection and initial treatment mix were evaluated descriptively. Separate multivariate logistic regression models for colon (CC and rectal cancer (RC patients were estimated to isolate the independent effects of these factors along with technological change (proxied by cohort year on 5-year survival. Results Our sample included 37,808 CC and 13,619 RC patients (combined mean ± SD age: 77.2 ± 7.0 years; 55% female; 87% white. In recent years, more CC patients were diagnosed at Stage I and fewer at Stages II and IV, and more RC patients were diagnosed at Stage I and fewer at Stages II and III. CC and RC patients diagnosed in later years were slightly older with somewhat better Charlson scores and were more likely to be female, from the Northeast, and from areas with higher average education levels. Surgery alone was more common in later years for CC patients while combined surgery, chemotherapy, and radiotherapy was more common for RC patients. Between 1992 and 2000, 5-year observed survival improved from 43.0% to 46.3% for CC patients and from 39.4% to 42.2% for RC patients. Multivariate logistic regressions indicate that patients diagnosed in 2000 had significantly greater odds of 5-year survival than those diagnosed in 1992 (OR: 1.35 for CC, 1.38 for RC. Our decomposition suggests that early detection had little impact on survival; rather, technological improvements (e.g., new medical technologies or more effective use of existing technologies and changing demographics were responsible for the largest share of the change in 5

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

KAUST Repository

Rubio, Francisco J.

2016-02-09

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

A comparison of multivariate genome-wide association methods

DEFF Research Database (Denmark)

Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

2014-01-01

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

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Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Histopathological analysis of pre-implantation donor kidney biopsies: association with graft survival and function in one year post-transplantation

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Karla Lais Pêgas

2014-04-01

Full Text Available Introduction: Pre-implantation kidney biopsy is a decision-making tool when considering the use of grafts from deceased donors with expanded criteria, implanting one or two kidneys and comparing this to post-transplantation biopsies. The role of histopathological alterations in kidney compartments as a prognostic factor in graft survival and function has had conflicting results. Objective: This study evaluated the prevalence of chronic alterations in pre-implant biopsies of kidney grafts and the association of findings with graft function and survival in one year post-transplant. Methods: 110 biopsies were analyzed between 2006 and 2009 at Santa Casa de Porto Alegre, including live donors, ideal deceased donors and those with expanded criteria. The score was computed according to criteria suggested by Remuzzi. The glomerular filtration rate (GFR was calculated using the abbreviated MDRD formula. Results: No statistical difference was found in the survival of donors stratified according to Remuzzi criteria. The GFR was significantly associated with the total scores in the groups with mild and moderate alterations, and in the kidney compartments alone, by univariate analysis. The multivariate model found an association with the presence of arteriosclerosis, glomerulosclerosis, acute rejection and delayed graft function. Conclusion: Pre-transplant chronic kidney alterations did not influence the post-transplantation one-year graft survival, but arteriosclerosis and glomerulosclerosis is predictive of a worse GFR. Delayed graft function and acute rejection are independent prognostic factors.

Endometriosis is the independent prognostic factor for survival in Chinese patients with epithelial ovarian carcinoma.

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Ren, Tong; Wang, Shu; Sun, Jian; Qu, Ji-Min; Xiang, Yang; Shen, Keng; Lang, Jing He

2017-10-03

Clinico-pathological characteristics and possible prognostic factors among women with epithelial ovarian carcinoma (EOC) with or without concurrent endometriosis were explored. We retrospectively identified 304 patients with EOC treated primarily at Peking Union Medical College Hospital with median follow-up time of 60 months. Of 304 patients with EOC, concurrent endometriosis was identified in 69 (22.7%). The patients with concurrent endometriosis were younger and more probably post-menopausal at onset, were less likely to have abdominal distension, with significantly lower level of pre-surgery serum Ca125 and less possibility of having the history of tubal ligation. The women with concurrent endometriosis group were more likely to have early stage tumors (88.41% versus 52.77%), receive optimal cytoreductive surgery (92.75% versus 71.06%), and less likely to have lymph node metastasis or to develop platinum resistance disease (7.25% versus 14.89%, and 7.35% versus 20%), when compared with women without coexisting endometriosis. The univariate analysis showed that concurrent endometriosis was a prognostic factor for overall survival (OS) and disease-free survival (DFS), but this association just remained in the DFS by multivariate analysis. Besides, multivariate analysis also showed that FIGO stage, residual disease, chemotherapy cycles, chemotherapy resistance and concomitant hypertension were the independent impact factors of OS for EOC patients; whereas FIGO stage, lymphadenectomy, residual disease, coexisting endometriosis and chemoresistance were independent impact factors of DFS for those patients. EOC patients with concurrent endometriosis showed distinct characteristics and had longer overall survival and disease-free survival when compared with those without endometriosis. Endometriosis was the independent prognostic factor for DFS for patients in this series.

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1975-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

Similar survival of patients with multiple versus single primary melanomas based on Utah Surveillance, Epidemiology, and End Results data (1973-2011).

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Grossman, Douglas; Farnham, James M; Hyngstrom, John; Klapperich, Marki E; Secrest, Aaron M; Empey, Sarah; Bowen, Glen M; Wada, David; Andtbacka, Robert H I; Grossmann, Kenneth; Bowles, Tawnya L; Cannon-Albright, Lisa A

2018-03-01

Survival data are mixed comparing patients with multiple primary melanomas (MPM) to those with single primary melanomas (SPM). We compared MPM versus SPM patient survival using a matching method that avoids potential biases associated with other analytic approaches. Records of 14,138 individuals obtained from the Surveillance, Epidemiology, and End Results registry of all melanomas diagnosed or treated in Utah between 1973 and 2011 were reviewed. A single matched control patient was selected randomly from the SPM cohort for each MPM patient, with the restriction that they survived at least as long as the interval between the first and second diagnoses for the matched MPM patient. Survival curves (n = 887 for both MPM and SPM groups) without covariates showed a significant survival disadvantage for MPM patients (chi-squared 39.29, P < .001). However, a multivariate Cox proportional hazards model showed no significant survival difference (hazard ratio 1.07, P = .55). Restricting the multivariate analysis to invasive melanomas also showed no significant survival difference (hazard ratio 0.99, P = .96). Breslow depth, ulceration status, and specific cause of death were not available for all patients. Patients with MPM had similar survival times as patients with SPM. Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Perioperative Blood Transfusion as a Significant Predictor of Biochemical Recurrence and Survival after Radical Prostatectomy in Patients with Prostate Cancer.

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Jung Kwon Kim

Full Text Available There have been conflicting reports regarding the association of perioperative blood transfusion (PBT with oncologic outcomes including recurrence rates and survival outcomes in prostate cancer. We aimed to evaluate whether perioperative blood transfusion (PBT affects biochemical recurrence-free survival (BRFS, cancer-specific survival (CSS, and overall survival (OS following radical prostatectomy (RP for patients with prostate cancer.A total of 2,713 patients who underwent RP for clinically localized prostate cancer between 1993 and 2014 were retrospectively analyzed. We performed a comparative analysis based on receipt of transfusion (PBT group vs. no-PBT group and transfusion type (autologous PBT vs. allogeneic PBT. Univariate and multivariate Cox-proportional hazard regression analysis were performed to evaluate variables associated with BRFS, CSS, and OS. The Kaplan-Meier method was used to calculate survival estimates for BRFS, CSS, and OS, and log-rank test was used to conduct comparisons between the groups.The number of patients who received PBT was 440 (16.5%. Among these patients, 350 (79.5% received allogeneic transfusion and the other 90 (20.5% received autologous transfusion. In a multivariate analysis, allogeneic PBT was found to be statistically significant predictors of BRFS, CSS, and OS; conversely, autologous PBT was not. The Kaplan-Meier survival analysis showed significantly decreased 5-year BRFS (79.2% vs. 70.1%, log-rank, p = 0.001, CSS (98.5% vs. 96.7%, log-rank, p = 0.012, and OS (95.5% vs. 90.6%, log-rank, p < 0.001 in the allogeneic PBT group compared to the no-allogeneic PBT group. In the autologous PBT group, however, none of these were statistically significant compared to the no-autologous PBT group.We found that allogeneic PBT was significantly associated with decreased BRFS, CSS, and OS. This provides further support for the immunomodulation hypothesis for allogeneic PBT.

Causal inference in survival analysis using pseudo-observations.

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Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-07-30

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Evaluation of functional outcome of the floating knee injury using multivariate analysis.

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Yokoyama, Kazuhiko; Tsukamoto, Tatsuro; Aoki, Shinichi; Wakita, Ryuji; Uchino, Masataka; Noumi, Takashi; Fukushima, Nobuaki; Itoman, Moritoshi

2002-11-01

The objective of this study is to evaluate significant contributing factors affecting the functional prognosis of floating knee injuries using multivariate analysis. A total of 68 floating knee injuries (67 patients) were treated at Kitasato University Hospital from 1986 to 1999. Both the femoral fractures and the tibial fractures were managed surgically by various methods. The functional results of these injuries were evaluated using the grading system of Karlström and Olerud. Follow-up periods ranged from 2 to 19 years (mean 50.2 months) after the original injury. We defined satisfactory (S) outcomes as those cases with excellent or good results and unsatisfactory (US) outcomes as those cases with acceptable or poor results. Logistic regression analysis was used as a multivariate analysis, and the dependent variables were defined as a satisfactory outcome or as an unsatisfactory outcome. The explanatory variables were predicting factors influencing the functional outcome such as age at trauma, gender, severity of soft-tissue injury in the femur and the tibia, AO fracture grade in the femur and the tibia, Fraser type (type I or type II), Injury Severity Score (ISS), and fixation time after injury (less than 1 week or more than 1 week) in the femur and the tibia. The final functional results were as follows: 25 cases had excellent results, 15 cases good results, 16 cases acceptable results, and 12 cases poor results. The predictive logistic regression equation was as follows: Log 1-p/p = 3.12-1.52 x Fraser type - 1.65 x severity of soft-tissue injury in the tibia - 1.31 x fixation time after injury in the tibia - 0.821 x AO fracture grade in the tibia + 1.025 x fixation time after injury in the femur - 0.687 x AO fracture grade in the femur ( p=0.01). Among the variables, Fraser type and the severity of soft-tissue injury in the tibia were significantly related to the final result. The multivariate analysis showed that both the involvement of the knee joint and

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas

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Pyka, Thomas; Hiob, Daniela; Wester, Hans-Juergen [Klinikum Rechts der Isar der TU Muenchen, Department of Nuclear Medicine, Munich (Germany); Gempt, Jens; Ringel, Florian; Meyer, Bernhard [Klinikum Rechts der Isar der TU Muenchen, Neurosurgic Department, Munich (Germany); Schlegel, Juergen [Klinikum Rechts der Isar der TU Muenchen, Institute of Pathology and Neuropathology, Munich (Germany); Bette, Stefanie [Klinikum Rechts der Isar der TU Muenchen, Neuroradiologic department, Munich (Germany); Foerster, Stefan [Klinikum Rechts der Isar der TU Muenchen, Department of Nuclear Medicine, Munich (Germany); Klinikum Rechts der Isar der TU Muenchen, TUM Neuroimaging Center (TUM-NIC), Munich (Germany)

2016-01-15

Amino acid positron emission tomography (PET) with [18F]-fluoroethyl-L-tyrosine (FET) is well established in the diagnostic work-up of malignant brain tumors. Analysis of FET-PET data using tumor-to-background ratios (TBR) has been shown to be highly valuable for the detection of viable hypermetabolic brain tumor tissue; however, it has not proven equally useful for tumor grading. Recently, textural features in 18-fluorodeoxyglucose-PET have been proposed as a method to quantify the heterogeneity of glucose metabolism in a variety of tumor entities. Herein we evaluate whether textural FET-PET features are of utility for grading and prognostication in patients with high-grade gliomas. One hundred thirteen patients (70 men, 43 women) with histologically proven high-grade gliomas were included in this retrospective study. All patients received static FET-PET scans prior to first-line therapy. TBR (max and mean), volumetric parameters and textural parameters based on gray-level neighborhood difference matrices were derived from static FET-PET images. Receiver operating characteristic (ROC) and discriminant function analyses were used to assess the value for tumor grading. Kaplan-Meier curves and univariate and multivariate Cox regression were employed for analysis of progression-free and overall survival. All FET-PET textural parameters showed the ability to differentiate between World Health Organization (WHO) grade III and IV tumors (p < 0.001; AUC 0.775). Further improvement in discriminatory power was possible through a combination of texture and metabolic tumor volume, classifying 85 % of tumors correctly (AUC 0.830). TBR and volumetric parameters alone were correlated with tumor grade, but showed lower AUC values (0.644 and 0.710, respectively). Furthermore, a correlation of FET-PET texture but not TBR was shown with patient PFS and OS, proving significant in multivariate analysis as well. Volumetric parameters were predictive for OS, but this correlation did not

Application of instrumental neutron activation analysis and multivariate statistical methods to archaeological Syrian ceramics

International Nuclear Information System (INIS)

Bakraji, E. H.; Othman, I.; Sarhil, A.; Al-Somel, N.

2002-01-01

Instrumental neutron activation analysis (INAA) has been utilized in the analysis of thirty-seven archaeological ceramics fragment samples collected from Tal AI-Wardiate site, Missiaf town, Hamma city, Syria. 36 chemical elements were determined. These elemental concentrations have been processed using two multivariate statistical methods, cluster and factor analysis in order to determine similarities and correlation between the various samples. Factor analysis confirms that samples were correctly classified by cluster analysis. The results showed that samples can be considered to be manufactured using three different sources of raw material. (author)

Accelerated fractionation in cancers of the esophagus: a multivariate analysis on 102 patients

International Nuclear Information System (INIS)

Girinsky, T.; Marsiglia, H.; Auperin, A.

1995-01-01

for T1, T2, T3 are 67%, 43%, and 25% respectively. Late toxicity included esophageal stricture and pulmonary fibrosis in 8% and 9% of the patients respectively. Multivariate analysis found that stage, the Karnofsky index, chemotherapy, and overall treatment time were independent prognostic factors for the cause specific survival. Conclusions: Accelerated fractionation for cancer of the esophagus seems to be useful in small tumors. Chemotherapy was found to be beneficial to patients when cause specific survival was the end point. Overall radiation treatment time was also an independent prognostic factor for the same end point

Accelerated fractionation in cancers of the esophagus: a multivariate analysis on 102 patients

Energy Technology Data Exchange (ETDEWEB)

Girinsky, T; Marsiglia, H; Auperin, A

1995-07-01

for T1, T2, T3 are 67%, 43%, and 25% respectively. Late toxicity included esophageal stricture and pulmonary fibrosis in 8% and 9% of the patients respectively. Multivariate analysis found that stage, the Karnofsky index, chemotherapy, and overall treatment time were independent prognostic factors for the cause specific survival. Conclusions: Accelerated fractionation for cancer of the esophagus seems to be useful in small tumors. Chemotherapy was found to be beneficial to patients when cause specific survival was the end point. Overall radiation treatment time was also an independent prognostic factor for the same end point.

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

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Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

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Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

2018-03-01

Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

Survival analysis of a treatment data for cancer of the larynx

International Nuclear Information System (INIS)

Khan, K.

2002-01-01

In this paper a survival analysis of the survival time is done. The Cox regression model is fitted to the survival time with the assumption of proportional hazard. A model is selected after inclusion and exclusion of factors and variables as explanatory variables. The assumption of proportional hazards is tested in the manner suggested by Harrell (1986). The assumption of proportional hazards is supported by these tests. However the plot of Schoenfeld residuals against dose gave a little evidence of non validity of the proportional hazard assumption. The assumption seems to be satisfied for variable time. The martingale residuals suggest no pattern for variable age. The functional form of dose is not linear. Hence the quadratic dose is used as an explanatory variable. A comparison of logistic regression analysis and survival analysis is also made in this paper. It can be concluded that Cox proportional hazards model is a better model than the logistic model as it is more parsimonious and utilizes more information. (author)

Lymph Node Yield as a Predictor of Survival in Pathologically Node Negative Oral Cavity Carcinoma.

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Lemieux, Aaron; Kedarisetty, Suraj; Raju, Sharat; Orosco, Ryan; Coffey, Charles

2016-03-01

Even after a pathologically node-negative (pN0) neck dissection for oral cavity squamous cell carcinoma (SCC), patients may develop regional recurrence. In this study, we (1) hypothesize that an increased number of lymph nodes removed (lymph node yield) in patients with pN0 oral SCC predicts improved survival and (2) explore predictors of survival in these patients using a multivariable model. Case series with chart review. Administrative database analysis. The SEER database was queried for patients diagnosed with all-stage oral cavity SCC between 1988 and 2009 who were determined to be pN0 after elective lymph node dissection. Demographic and treatment variables were extracted. The association of lymph node yield with 5-year all-cause survival was studied with multivariable survival analyses. A total of 4341 patients with pN0 oral SCC were included in this study. The 2 highest lymph node yield quartiles (representing >22 nodes removed) were found to be significant predictors of overall survival (22-35 nodes: hazard ratio [HR] = 0.854, P = .031; 36-98 nodes: HR = 0.827, P = .010). Each additional lymph node removed during neck dissection was associated with increased survival (HR = 0.995, P = .022). These data suggest that patients with oral SCC undergoing elective neck dissection may experience an overall survival benefit associated with greater lymph node yield. Mechanisms behind the demonstrated survival advantage are unknown. Larger nodal dissections may remove a greater burden of microscopic metastatic disease, diminishing the likelihood of recurrence. Lymph node yield may serve as an objective measure of the adequacy of lymphadenectomy. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

A method of signal transmission path analysis for multivariate random processes

International Nuclear Information System (INIS)

Oguma, Ritsuo

1984-04-01

A method for noise analysis called ''STP (signal transmission path) analysis'' is presentd as a tool to identify noise sources and their propagation paths in multivariate random proceses. Basic idea of the analysis is to identify, via time series analysis, effective network for the signal power transmission among variables in the system and to make use of its information to the noise analysis. In the present paper, we accomplish this through two steps of signal processings; first, we estimate, using noise power contribution analysis, variables which have large contribution to the power spectrum of interest, and then evaluate the STPs for each pair of variables to identify STPs which play significant role for the generated noise to transmit to the variable under evaluation. The latter part of the analysis is executed through comparison of partial coherence function and newly introduced partial noise power contribution function. This paper presents the procedure of the STP analysis and demonstrates, using simulation data as well as Borssele PWR noise data, its effectiveness for investigation of noise generation and propagation mechanisms. (author)

Biodegradable blends of starch/polyvinyl alcohol/glycerol: multivariate analysis of the mechanical properties

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Juliano Zanela

Full Text Available Abstract The aim of the work was to study the mechanical properties of extruded starch/polyvinyl alcohol (PVA/glycerol biodegradable blends using multivariate analysis. The blends were produced as cylindrical strands by extrusion using PVAs with different hydrolysis degrees and viscosities, at two extrusion temperature profiles (90/170/170/170/170 °C and 90/170/200/200/200 °C and three conditioning relative humidities of the samples (33, 53, and 75%. The mechanical properties showed a great variability according to PVA type, as well as the extrusion temperature profile and the conditioning relative humidity; the tensile strength ranged from 0.42 to 5.40 MPa, elongation at break ranged from 10 to 404% and Young’s modulus ranged from 0.93 to 13.81 MPa. The multivariate analysis was a useful methodology to study the mechanical properties behavior of starch/PVA/glycerol blends, and it can be used as an exploratory technique to select of the more suitable PVA type and extrusion temperature to produce biodegradable materials.

Impact of Marital Status on Tumor Stage at Diagnosis and on Survival in Male Breast Cancer.

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Adekolujo, Orimisan Samuel; Tadisina, Shourya; Koduru, Ujwala; Gernand, Jill; Smith, Susan Jane; Kakarala, Radhika Ramani

2017-07-01

The effect of marital status (MS) on survival varies according to cancer type and gender. There has been no report on the impact of MS on survival in male breast cancer (MBC). This study aims to determine the influence of MS on tumor stage at diagnosis and survival in MBC. Men with MBC ≥18 years of age in the SEER database from 1990 to 2011 were included in the study. MS was classified as married and unmarried (including single, divorced, separated, widowed). Kaplan-Meier method was used to estimate the 5-year cancer-specific survival. Multivariate regression analyses were done to determine the effect of MS on presence of Stage IV disease at diagnosis and on cancer-specific mortality. The study included 3,761 men; 2,647 (70.4%) were married. Unmarried men were more often diagnosed with Stage IV MBC compared with married (10.7% vs. 5.5%, p Unmarried men (compared with married) were significantly less likely to undergo surgery (92.4% vs. 96.7%, p unmarried males with Stages II, III, and IV MBC have significantly worse 5-year cancer-specific survival compared with married. On multivariate analysis, being unmarried was associated with increased hazard of death (HR = 1.43, p Unmarried males with breast cancer are at greater risk for Stage IV disease at diagnosis and poorer outcomes compared with married males.

Marital status and survival in patients with rectal cancer: An analysis of the Surveillance, Epidemiology and End Results (SEER) database.

Science.gov (United States)

Wang, Xiangyang; Cao, Weilan; Zheng, Chenguo; Hu, Wanle; Liu, Changbao

2018-06-01

Marital status has been validated as an independent prognostic factor for survival in several cancer types, but is controversial in rectal cancer (RC). The objective of this study was to investigate the impact of marital status on the survival outcomes of patients with RC. We extracted data of 27,498 eligible patients diagnosed with RC between 2004 and 2009 from the Surveillance, Epidemiology and End Results (SEER) database. Patients were categorized into married, never married, divorced/separated and widowed groups.We used Chi-square tests to compare characteristics of patients with different marital status.Rectal cancer specific survival was compared using the Kaplan-Meier method,and multivariate Cox regression analyses was used to analyze the survival outcome risk factors in different marital status. The widowed group had the highest percentage of elderly patients and women,higher proportion of adenocarcinomas, and more stage I/II in tumor stage (P married group (76.7% VS 85.4%). Compared with the married patients, the never married (HR 1.40), widowed (HR 1.61,) and divorced/separated patients (HR 1.16) had an increased overall 5-year mortality. A further analysis showed that widowed patients had an increased overall 5-year cause-specific survival(CSS) compared with married patients at stage I(HR 1.92),stage II (HR 1.65),stage III (HR 1.73),and stage IV (HR 1.38). Our study showed marriage was associated with better outcomes of RC patients, but unmarried RC patients, especially widowed patients,are at greater risk of cancer specific mortality. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

NARCIS (Netherlands)

Molenaar, P.C.M.

1987-01-01

Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic

Principal response curves: analysis of time-dependent multivariate responses of biological community to stress

NARCIS (Netherlands)

Brink, van den P.J.; Braak, ter C.J.F.

1999-01-01

In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity

Effect of Thoracic Surgeons on Lung Cancer Patients’ Survival

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Ning LI

2018-02-01

Full Text Available Background and objective Surgeons are the direct decision-makers and performers in the surgical treatment of patients with lung cancer. Whether the differences among doctors affect the survival of patients is unclear. This study analyzed the five-year survival rates of different thoracic surgeries in patients undergoing surgery to assess the physician's impact and impact. Methods A retrospective analysis of five years between 2002-2007 in the Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, for surgical treatment of lung cancer patients. According to different surgeons grouping doctors to compare the basic information of patients, surgical methods, short-term results and long-term survival differences. Results A total of 712 patients treated by 11 experienced thoracic surgeons were included in this study. The patients have nosignificant difference with gender, age, smoking, pathological type between groups. There were significant differences in clinical staging, surgery type, operation time, blood transfusion rate, number of lymph node dissection, palliative resection rate, postoperative complications and perioperative mortality. There was a significant difference in five-year survival rates among patients treated by different doctors. This difference can be seen in all clinical stage analyzes with consistency. In the multivariate analysis, it was suggested that surgeon was an independent factor influencing the prognosis of patients. Conclusion Thoracic surgeon has a significant effect on the therapeutic effect of lung cancer patients.

Diabetes mellitus may affect the long-term survival of hepatitis B virus-related hepatocellular carcinoma patients after liver transplantation.

Science.gov (United States)

Zhang, Qing; Deng, Yong-Lin; Liu, Chang; Huang, Li-Hong; Shang, Lei; Chen, Xin-Guo; Wang, Le-Tian; Du, Jin-Zan; Wang, Ying; Wang, Pei-Xiao; Zhang, Hui; Shen, Zhong-Yang

2016-11-21

To determine whether diabetes mellitus (DM) affects prognosis/recurrence after liver transplantation (LT) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). A retrospective study was conducted between January 2000 and August 2013 on 1631 patients with HBV-related HCC who underwent LT with antiviral prophylaxis. Patient data were obtained from the China Liver Transplant Registry (https://www.cltr.org/). To compare the outcomes and tumor recurrence in the HBV-related HCC patients with or without DM, statistical analyses were conducted using χ 2 tests, Mann-Whitney tests, the Kaplan-Meier method, log-rank tests and multivariate step-wise Cox regression analysis. Univariate analysis of 1631 patients who underwent LT found overall 1-, 3- and 5-year survival rates of 79%, 73% and 71% respectively in the DM patients, and 84%, 78% and 76% in the non-DM patients respectively. Overall survival rate differences after LT between the two groups were significant ( P = 0.041), but recurrence-free survival rates were not ( P = 0.096). By stratified analysis, the overall survival rates in DM patients for age > 50 years ( P = 0.002), the presence of vascular invasion ( P = 0.096), tumors ≤ 3 cm ( P = 0.047), two to three tumor nodules ( P = 0.007), Child-Pugh grade B ( P = 0.018), and pre-LT alanine aminotransferase levels between 40 and 80 IU/L ( P = 0.017) were significantly lower than in non-DM patients. Additionally, serum α-fetoprotein level > 2000 ng/mL ( P = 0.052) was associated with a significant survival difference trend between DM and non-DM patients. Multivariate analysis showed that the presence of DM ( P < 0.001, HR = 1.591; 95%CI: 1.239-2.041) was an independent predictor associated with poor survival after LT. HBV-related HCC patients with DM have decreased long-term overall survival and poor LT outcomes. Prevention strategies for HCC patients with DM are recommended.

Morphological analysis of enlarged ventricle on CT image, using multivariate analysis

International Nuclear Information System (INIS)

Iwasaki, Satoru; Kichikawa, Kimihiko; Otsuji, Hideyuki; Fukusumi, Akio; Kobayashi, Yasuo.

1983-01-01

Multivariate analysis of enlarged cerebral ventricle on CT was undertaken to study the characteristics of ventricular morphology. Several ventricular segments of enlarged ventricle, defined on the basis of the study of normal group, were linearly measured on CT image. Then the discriminant analysis with the increase and decrease of variable was applied. The following are the results obtained. The error ratio of discrimination between pressure hydrocephalus and cerebral atrophy was 8.4 %, and between obstructive hydrocephalus and communicating hydrocephalus was 11.3 %. Ventricular segments were divided into three groups according to their character of enlargement: (1) the temporal horn and trigone are large in pressure hydrocephalus; (2) the hypothalamic segment of the third ventricle and the body of lateral ventricle are larger in obstructive hydrocephalus than in communicating hydrocephalus; (3) the anterior horn, cellae mediae at the level of the head of caudate nuclei and thalamic segment of the third ventricle are relatively large in cerebral atrophy and communicating hydrocephalus. The hypothalamic segment of the third ventricle assumes a round or oval shape in pressure hydrocephalus but a rectangular or teardrop shape in cerebral atrophy. These findings are contributory to pathological evaluation of ventricular enlargement. (author)

Multivariate analysis for customer segmentation based on RFM

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Álvaro Julio Cuadros López

2018-02-01

Full Text Available Context: To build a successful relationship management (CRM, companies must start with the identification of the true value of customers, as this provides basic information to implement more targeted and customized marketing strategies. The RFM methodology, a classic analysis tool that uses three evaluation parameters, allows companies to understand customer behavior, and to establish customer segments. The addition of a new parameter in the traditional technique is an opportunity to refine the possible outcomes of a customer segmentation since it not only provides a new element of evaluation to identify the most valuable customers, but it also makes it possible to differentiate and get to know customers even better. Method: The article presents a methodology that allows to establish customer segments using an extended RFM method with new variables, selected through multivariate analysis..  Results: The proposed implementation was applied in a company in which variables such as profit, profit percentage, and billing due date were tested. Therefore, it was possible to establish a more detailed customer segmentation than with the classic RFM. Conclusions: the RFM analysis is a method widely used in the industry for its easy understanding and applicability. However, it can be improved with the use of statistical procedures and new variables, which will allow companies to have deeper information about the behavior of the clients, and will facilitate the design of specific marketing strategies.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

Science.gov (United States)

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients

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M. Agostini

2013-01-01

Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

Reduced in-hospital survival rates of out-of-hospital cardiac arrest victims with obstructive pulmonary disease

DEFF Research Database (Denmark)

Blom, M T; Warnier, M J; Bardai, A

2013-01-01

) had comparable survival to ER (75% vs. 78%, OR 0.9 [95% CI: 0.6-1.3]) and to hospital admission (56% vs. 57%, OR 1.0 [0.7-1.4]). However, survival to hospital discharge was significantly lower among OPD patients (21% vs. 33%, OR 0.6 [0.4-0.9]). Multivariate regression analysis among patients who were...... with obstructive pulmonary disease (OPD) have a lower survival rate after OHCA than non-OPD patients. METHODS: We performed a community-based cohort study of 1172 patients with non-traumatic OHCA with ECG-documented VT/VF between 2005 and 2008. We compared survival to emergency room (ER), to hospital admission...... admitted to hospital (OPD: n=100, no OPD: n=561) revealed that OPD was an independent determinant of reduced 30-day survival rate (39% vs. 59%, adjusted OR 0.6 [0.4-1.0, p=0.035]). CONCLUSION: OPD-patients had lower survival rates after OHCA than non-OPD patients. Survival to ER and to hospital admission...

A Framework for RFID Survivability Requirement Analysis and Specification

Science.gov (United States)

Zuo, Yanjun; Pimple, Malvika; Lande, Suhas

Many industries are becoming dependent on Radio Frequency Identification (RFID) technology for inventory management and asset tracking. The data collected about tagged objects though RFID is used in various high level business operations. The RFID system should hence be highly available, reliable, and dependable and secure. In addition, this system should be able to resist attacks and perform recovery in case of security incidents. Together these requirements give rise to the notion of a survivable RFID system. The main goal of this paper is to analyze and specify the requirements for an RFID system to become survivable. These requirements, if utilized, can assist the system in resisting against devastating attacks and recovering quickly from damages. This paper proposes the techniques and approaches for RFID survivability requirements analysis and specification. From the perspective of system acquisition and engineering, survivability requirement is the important first step in survivability specification, compliance formulation, and proof verification.

Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

Energy Technology Data Exchange (ETDEWEB)

Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

2011-09-30

Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known

Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

International Nuclear Information System (INIS)

Rodrigues, J.A.; Barros, A.S.; Carvalho, B.; Brandao, T.; Gil, Ana M.

2011-01-01

Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: · Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. · Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. · Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. · 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5

Synthetic environmental indicators: A conceptual approach from the multivariate statistics

International Nuclear Information System (INIS)

Escobar J, Luis A

2008-01-01

This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis

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Wondimu Ahmed

2017-09-01

Full Text Available This study investigated the relationship between motivation and self-regulated learning (SRL in a nationally representative sample of 5245, 15-year-old students in the USA. A multivariate multilevel analysis was conducted to examine the role of three motivational variables (self-efficacy, intrinsic value & instrumental value in predicting three SRL strategies (memorization, elaboration & control. The results showed that compared to self-efficacy, intrinsic value and instrumental value of math were stronger predictors of memorization, elaboration and control strategies. None of the motivational variables had a stronger effect on one strategy than the other. The findings suggest that the development of self-regulatory skills in math can be greatly enhanced by helping students develop positive value of and realistic expectancy for success in math.

A taylor series approach to survival analysis

International Nuclear Information System (INIS)

Brodsky, J.B.; Groer, P.G.

1984-09-01

A method of survival analysis using hazard functions is developed. The method uses the well known mathematical theory for Taylor Series. Hypothesis tests of the adequacy of many statistical models, including proportional hazards and linear and/or quadratic dose responses, are obtained. A partial analysis of leukemia mortality in the Life Span Study cohort is used as an example. Furthermore, a relatively robust estimation procedure for the proportional hazards model is proposed. (author)

Multivariate co-integration analysis of the Kaya factors in Ghana.

Science.gov (United States)

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-05-01

The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

Factors associated with survival of epiploic foramen entrapment colic: a multicentre, international study.

Science.gov (United States)

Archer, D C; Pinchbeck, G L; Proudman, C J

2011-08-01

Epiploic foramen entrapment (EFE) has been associated with reduced post operative survival compared to other types of colic but specific factors associated with reduced long-term survival of these cases have not been evaluated in a large number of horses using survival analysis. To describe post operative survival of EFE cases and to identify factors associated with long-term survival. A prospective, multicentre, international study was conducted using clinical data and long-term follow-up information for 126 horses diagnosed with EFE during exploratory laparotomy at 15 clinics in the UK, Ireland and USA. Descriptive data were generated and survival analysis performed to identify factors associated with reduced post operative survival. For the EFE cohort that recovered following anaesthesia, survival to hospital discharge was 78.5%. Survival to 1 and 2 years post operatively was 50.6 and 34.3%, respectively. The median survival time of EFE cases undergoing surgery was 397 days. Increased packed cell volume (PCV) and increased length of small intestine (SI) resected were significantly associated with increased likelihood of mortality when multivariable analysis of pre- and intraoperative variables were analysed. When all pre-, intra- and post operative variables were analysed separately, only horses that developed post operative ileus (POI) were shown to be at increased likelihood of mortality. Increased PCV, increased length of SI resected and POI are all associated with increased likelihood of mortality of EFE cases. This emphasises the importance of early diagnosis and treatment and the need for improved strategies in the management of POI in order to reduce post operative mortality in these cases. The present study provides evidence-based information to clinicians and owners of horses undergoing surgery for EFE about long-term survival. These results are applicable to university and large private clinics over a wide geographical area. © 2011 EVJ Ltd.

Analysis of audiometric relapse-free survival in patients with immune-mediated hearing loss exclusively treated with corticosteroids.

Science.gov (United States)

Mata-Castro, Nieves; García-Chilleron, Raimon; Gavilanes-Plasencia, Javier; Ramírez-Camacho, Rafael; García-Fernández, Alfredo; García-Berrocal, José Ramón

2017-10-12

To describe the results in terms of audiometric relapse-free survival and relapse rate in immunomediated hearing loss patients treated exclusively with corticosteroids. Retrospective study of patients with audiometric relapses, monitored from 1995 to 2014, in two centres of the Community of Madrid. We evaluated 31 patients with a mean age of 48.52 years (14.67 SD), of which 61.3% were women. Most hearing loss was fluctuating (48.4%). Only 16.1% of patients had systemic autoimmune disease. There is a moderate positive correlation between the sex variable and the systemic involvement variable (Spearman's correlation coefficient=0.356): specifically, between being female and systemic disease. The relative incidence rate of relapse in the first year was 2.01 relapses/year with a 95% CI (1.32 to 2.92). The mean survival time of the event (audiometric relapse) was 5.25 months (SD 0.756). With multivariate analysis, the only variable that achieved statistical significance was age, with a hazard ratio of 1.032 (95% CI; 1.001-1.063, P=.043). Immune-mediated disease of the inner ear is a chronic disease with relapses. Half of the patients with immunomediated hearing loss treated exclusively with corticosteroids relapse before 6 months of follow-up. In addition, if a patient has not relapsed, they are more likely to relapse as each year passes. Analysis of the of audiometric relapse- free survival will enable the effect of future treatments to be compared and their capacity to reduce the rhythm of relapses. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Otorrinolaringología y Cirugía de Cabeza y Cuello. All rights reserved.

Preoperative diffusion-weighted imaging of single brain metastases correlates with patient survival times.

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Anna Sophie Berghoff

Full Text Available BACKGROUND: MRI-based diffusion-weighted imaging (DWI visualizes the local differences in water diffusion in vivo. The prognostic value of DWI signal intensities on the source images and apparent diffusion coefficient (ADC maps respectively has not yet been studied in brain metastases (BM. METHODS: We included into this retrospective analysis all patients operated for single BM at our institution between 2002 and 2010, in whom presurgical DWI and BM tissue samples were available. We recorded relevant clinical data, assessed DWI signal intensity and apparent diffusion coefficient (ADC values and performed histopathological analysis of BM tissues. Statistical analyses including uni- and multivariate survival analyses were performed. RESULTS: 65 patients (34 female, 31 male with a median overall survival time (OS of 15 months (range 0-99 months were available for this study. 19 (29.2% patients presented with hyper-, 3 (4.6% with iso-, and 43 (66.2% with hypointense DWI. ADCmean values could be determined in 32 (49.2% patients, ranged from 456.4 to 1691.8*10⁻⁶ mm²/s (median 969.5 and showed a highly significant correlation with DWI signal intensity. DWI hyperintensity correlated significantly with high amount of interstitial reticulin deposition. In univariate analysis, patients with hyperintense DWI (5 months and low ADCmean values (7 months had significantly worse OS than patients with iso/hypointense DWI (16 months and high ADCmean values (30 months, respectively. In multivariate survival analysis, high ADCmean values retained independent statistical significance. CONCLUSIONS: Preoperative DWI findings strongly and independently correlate with OS in patients operated for single BM and are related to interstitial fibrosis. Inclusion of DWI parameters into established risk stratification scores for BM patients should be considered.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-06

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-01

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response

International Nuclear Information System (INIS)

Skwarchuk, Mark W.; Jackson, Andrew; Zelefsky, Michael J.; Venkatraman, Ennapadam S.; Cowen, Didier M.; Levegruen, Sabine; Burman, Chandra M.; Fuks, Zvi; Leibel, Steven A.; Ling, C. Clifton

2000-01-01

Purpose: The purpose of this paper is to use the outcome of a dose escalation protocol for three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer to study the dose-response for late rectal toxicity and to identify anatomic, dosimetric, and clinical factors that correlate with late rectal bleeding in multivariate analysis. Methods and Materials: Seven hundred forty-three patients with T1c-T3 prostate cancer were treated with 3D-CRT with prescribed doses of 64.8 to 81.0 Gy. The 5-year actuarial rate of late rectal toxicity was assessed using Kaplan-Meier statistics. A retrospective dosimetric analysis was performed for patients treated to 70.2 Gy (52 patients) or 75.6 Gy (119 patients) who either exhibited late rectal bleeding (RTOG Grade 2/3) within 30 months after treatment (i.e., 70.2 Gy--13 patients, 75.6 Gy--36 patients) or were nonbleeding for at least 30 months (i.e., 70.2 Gy--39 patients, 75.6 Gy--83 patients). Univariate and multivariate logistic regression was performed to correlate late rectal bleeding with several anatomic, dosimetric, and clinical variables. Results: A dose response for ≥ Grade 2 late rectal toxicity was observed. By multivariate analysis, the following factors were significantly correlated with ≥ Grade 2 late rectal bleeding for patients prescribed 70.2 Gy: 1) enclosure of the outer rectal contour by the 50% isodose on the isocenter slice (i.e., Iso50) (p max (p max

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

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Wei-Liang Chen

2013-01-01

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

Multivariate methods in nuclear waste remediation: Needs and applications

International Nuclear Information System (INIS)

Pulsipher, B.A.

1992-05-01

The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs

The time dependent association of adrenaline administration and survival from out-of-hospital cardiac arrest.

Science.gov (United States)

Ewy, Gordon A; Bobrow, Bentley J; Chikani, Vatsal; Sanders, Arthur B; Otto, Charles W; Spaite, Daniel W; Kern, Karl B

2015-11-01

Recommended for decades, the therapeutic value of adrenaline (epinephrine) in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) is controversial. To investigate the possible time-dependent outcomes associated with adrenaline administration by Emergency Medical Services personnel (EMS). A retrospective analysis of prospectively collected data from a near statewide cardiac resuscitation database between 1 January 2005 and 30 November 2013. Multivariable logistic regression was used to analyze the effect of the time interval between EMS dispatch and the initial dose of adrenaline on survival. The primary endpoints were survival to hospital discharge and favourable neurologic outcome. Data from 3469 patients with witnessed OHCA were analyzed. Their mean age was 66.3 years and 69% were male. An initially shockable rhythm was present in 41.8% of patients. Based on a multivariable logistic regression model with initial adrenaline administration time interval (AATI) from EMS dispatch as the covariate, survival was greatest when adrenaline was administered very early but decreased rapidly with increasing (AATI); odds ratio 0.94 (95% Confidence Interval (CI) 0.92-0.97). The AATI had no significant effect on good neurological outcome (OR=0.96, 95% CI=0.90-1.02). In patients with OHCA, survival to hospital discharge was greater in those treated early with adrenaline by EMS especially in the subset of patients with a shockable rhythm. However survival rapidly decreased with increasing adrenaline administration time intervals (AATI). Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Geographic remoteness, area-level socioeconomic disadvantage and inequalities in colorectal cancer survival in Queensland: a multilevel analysis

Science.gov (United States)

2013-01-01

Background To explore the impact of geographical remoteness and area-level socioeconomic disadvantage on colorectal cancer (CRC) survival. Methods Multilevel logistic regression and Markov chain Monte Carlo simulations were used to analyze geographical variations in five-year all-cause and CRC-specific survival across 478 regions in Queensland Australia for 22,727 CRC cases aged 20–84 years diagnosed from 1997–2007. Results Area-level disadvantage and geographic remoteness were independently associated with CRC survival. After full multivariate adjustment (both levels), patients from remote (odds Ratio [OR]: 1.24, 95%CrI: 1.07-1.42) and more disadvantaged quintiles (OR = 1.12, 1.15, 1.20, 1.23 for Quintiles 4, 3, 2 and 1 respectively) had lower CRC-specific survival than major cities and least disadvantaged areas. Similar associations were found for all-cause survival. Area disadvantage accounted for a substantial amount of the all-cause variation between areas. Conclusions We have demonstrated that the area-level inequalities in survival of colorectal cancer patients cannot be explained by the measured individual-level characteristics of the patients or their cancer and remain after adjusting for cancer stage. Further research is urgently needed to clarify the factors that underlie the survival differences, including the importance of geographical differences in clinical management of CRC. PMID:24152961

What Explains the Survival Gap of Pushed and Pulled Corporate Spin-offs?

DEFF Research Database (Denmark)

Rocha, Vera; Carneiro, Anabela; Varum, Celeste

2015-01-01

Unconditionally, pushed spin-offs are found to survive longer than their pulled counterparts. Using matched employer-employee data and novel multivariate decomposition techniques, we show that pushed spin-offs’ relative survival advantage is mostly explained by their larger human capital endowments...

Effect of smoking on survival of patients with hepatocellular carcinoma.

Science.gov (United States)

Kolly, Philippe; Knöpfli, Marina; Dufour, Jean-François

2017-11-01

Lifestyle factors such as smoking, obesity and physical activity have gained interest in the field of hepatocellular carcinoma. These factors play a significant role in the development of hepatocellular carcinoma. Several studies revealed the impact of tobacco consumption on the development of hepatocellular carcinoma and its synergistic effects with viral etiologies (hepatitis B and C). The effects of smoking on survival in patients with a diagnosed hepatocellular carcinoma have not yet been investigated in a Western cohort where hepatitis C infection is a major risk factor. Using data from a prospective cohort of patients with hepatocellular carcinoma who were followed at the University Hospital of Bern, Switzerland, survival was compared by Kaplan-Meier analysis in smokers and nonsmokers, and multivariate Cox regression was applied to control for confounding variables. Of 238 eligible hepatocellular carcinoma patients, 64 were smokers at the time of inclusion and 174 were nonsmokers. Smokers had a significant worse overall survival than nonsmokers (hazard ratio 1.77, 95% confidence interval: 1.22-2.58, P=.003). Analysis of patients according to their underlying liver disease, revealed that smoking, and not nonsmoking, affected survival of hepatitis B virus and C virus-infected patients only. In this subgroup, smoking was an independent predictor for survival (hazard ratio 2.99, 95% confidence interval: 1.7-5.23, Phepatocellular carcinoma. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Is there racial/ethnic variance in cervical cancer- specific survival of ...

African Journals Online (AJOL)

incident cervical carcinoma, between 1992 and 1999, in the Surveillance Epidemiology and End Results (SEER) Data was linked with Medicare to examine the impact of race/ethnicity on overall and cancer-specific survival, using Kaplan Meier survival estimates and multivariable Cox Regression model. Results: There was ...

Prognostic classification index in Iranian colorectal cancer patients: Survival tree analysis

Directory of Open Access Journals (Sweden)

Amal Saki Malehi

2016-01-01

Full Text Available Aims: The aim of this study was to determine the prognostic index for separating homogenous subgroups in colorectal cancer (CRC patients based on clinicopathological characteristics using survival tree analysis. Methods: The current study was conducted at the Research Center of Gastroenterology and Liver Disease, Shahid Beheshti Medical University in Tehran, between January 2004 and January 2009. A total of 739 patients who already have been diagnosed with CRC based on pathologic report were enrolled. The data included demographic and clinical-pathological characteristic of patients. Tree-structured survival analysis based on a recursive partitioning algorithm was implemented to evaluate prognostic factors. The probability curves were calculated according to the Kaplan-Meier method, and the hazard ratio was estimated as an interest effect size. Result: There were 526 males (71.2% of these patients. The mean survival time (from diagnosis time was 42.46± (3.4. Survival tree identified three variables as main prognostic factors and based on their four prognostic subgroups was constructed. The log-rank test showed good separation of survival curves. Patients with Stage I-IIIA and treated with surgery as the first treatment showed low risk (median = 34 months whereas patients with stage IIIB, IV, and more than 68 years have the worse survival outcome (median = 9.5 months. Conclusion: Constructing the prognostic classification index via survival tree can aid the researchers to assess interaction between clinical variables and determining the cumulative effect of these variables on survival outcome.

Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

DEFF Research Database (Denmark)

Bro, R.; van den Berg, F.; Thybo, A.

2002-01-01

This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics-multivariate data analysis - it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems...

Multivariate quantitative structure-pharmacokinetic relationships (QSPKR) analysis of adenosine A(1) receptor agonists in rat

NARCIS (Netherlands)

Van der Graaf, PH; Nilsson, J; Van Schaick, EA; Danhof, M

The aim of this study was to investigate the feasibility of a quantitative structure-pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and

Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data.

Science.gov (United States)

Ultsch, Alfred; Lötsch, Jörn

2015-01-01

Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data. The method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.

ESC guidelines adherence is associated with improved survival in patients from the Norwegian Heart Failure Registry.

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De Blois, Jonathan; Fagerland, Morten Wang; Grundtvig, Morten; Semb, Anne Grete; Gullestad, Lars; Westheim, Arne; Hole, Torstein; Atar, Dan; Agewall, Stefan

2015-01-01

To assess the adherence to heart failure (HF) guidelines for angiotensin-converting enzyme-I (ACE-I), angiotensin II receptor blockers (ARB), and β-blockers and the possible association of ACE-I or ARB, β-blockers, and statins with survival in the large contemporary Norwegian Heart Failure Registry. The study included 5761 outpatients who were diagnosed with HF of any aetiology (mean left ventricular ejection fraction 32% ± 11%) from January 2000 to January 2010 and followed up until death or February 2010. Adherence to treatment according to the guidelines was high. Cox regression analysis to identify risk factors for all-cause mortality, after adjustment for many factors, showed that ACE-I ≥ 50% of target dose, use of beta-blockers, and statins were significantly related to improved survival (P = 0.003, P < 0.001, and P < 0.001, respectively). Propensity scoring showed the same benefit for these variables. Both multivariable and propensity scoring analyses showed survival benefits with β-blockers, statins, and adequate doses of ACE-I in this contemporary HF cohort. This study stresses the importance of guidelines adherence, even in the context of high levels of adherence to guidelines. Moreover, respecting the recommended target doses of ACE-I appears to have a crucial role in survival improvement and, in the multivariate Cox regression analysis, ARB treatment was not significantly associated with a lower all-cause mortality. Published on behalf of the European Society of Cardiology. All rights reserved. ©The Author 2015. For permissions please email: journals.permissions@oup.com.

Association between obesity with disease-free survival and overall survival in triple-negative breast cancer: A meta-analysis.

Science.gov (United States)

Mei, Lin; He, Lin; Song, Yuhua; Lv, Yang; Zhang, Lijiu; Hao, Fengxi; Xu, Mengmeng

2018-05-01

To investigate the relationship between obesity and disease-free survival (DFS) and overall survival (OS) of triple-negative breast cancer. Citations were searched in PubMed, Cochrane Library, and Web of Science. Random effect model meta-analysis was conducted by using Revman software version 5.0, and publication bias was evaluated by creating Egger regression with STATA software version 12. Nine studies (4412 patients) were included for DFS meta-analysis, 8 studies (4392 patients) include for OS meta-analysis. There were no statistical significances between obesity with DFS (P = .60) and OS (P = .71) in triple-negative breast cancer (TNBC) patients. Obesity has no impact on DFS and OS in patients with TNBC.

Multivariate data analysis as a fast tool in evaluation of solid state phenomena

DEFF Research Database (Denmark)

Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja

2006-01-01

of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets...... the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles...... or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information....

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

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Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Fifteen-Year Biochemical Relapse-Free Survival, Cause-Specific Survival, and Overall Survival Following I125 Prostate Brachytherapy in Clinically Localized Prostate Cancer: Seattle Experience

International Nuclear Information System (INIS)

Sylvester, John E.; Grimm, Peter D.; Wong, Jason; Galbreath, Robert W.; Merrick, Gregory; Blasko, John C.

2011-01-01

Purpose: To report 15-year biochemical relapse-free survival (BRFS), cause-specific survival (CSS), and overall survival (OS) outcomes of patients treated with I 125 brachytherapy monotherapy for clinically localized prostate cancer early in the Seattle experience. Methods and Materials: Two hundred fifteen patients with clinically localized prostate cancer were consecutively treated from 1988 to 1992 with I 125 monotherapy. They were prospectively followed as a tight cohort. They were evaluated for BRFS, CSS, and OS. Multivariate analysis was used to evaluate outcomes by pretreatment clinical prognostic factors. BRFS was analyzed by the Phoenix (nadir + 2 ng/mL) definition. CSS and OS were evaluated by chart review, death certificates, and referring physician follow-up notes. Gleason scoring was performed by general pathologists at a community hospital in Seattle. Time to biochemical failure (BF) was calculated and compared by Kaplan-Meier plots. Results: Fifteen-year BRFS for the entire cohort was 80.4%. BRFS by D'Amico risk group classification cohort analysis was 85.9%, 79.9%, and 62.2% for low, intermediate, and high-risk patients, respectively. Follow-up ranged from 3.6 to 18.4 years; median follow-up was 15.4 years for biochemically free of disease patients. Overall median follow-up was 11.7 years. The median time to BF in those who failed was 5.1 years. CSS was 84%. OS was 37.1%. Average age at time of treatment was 70 years. There was no significant difference in BRFS between low and intermediate risk groups. Conclusion: I 125 monotherapy results in excellent 15-year BRFS and CSS, especially when taking into account the era of treatment effect.

Multi-variable systems in nuclear power plant

International Nuclear Information System (INIS)

Collins, G.B.; Howell, J.

1982-01-01

Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)

Multivariate statistics high-dimensional and large-sample approximations

CERN Document Server

Fujikoshi, Yasunori; Shimizu, Ryoichi

2010-01-01

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

Mediation analysis of the relationship between institutional research activity and patient survival

DEFF Research Database (Denmark)

Rochon, Justine; du Bois, Andreas; Lange, Theis

2014-01-01

BACKGROUND: Recent studies have suggested that patients treated in research-active institutions have better outcomes than patients treated in research-inactive institutions. However, little attention has been paid to explaining such effects, probably because techniques for mediation analysis...... existing so far have not been applicable to survival data. METHODS: We investigated the underlying mechanisms using a recently developed method for mediation analysis of survival data. Our analysis of the effect of research activity on patient survival was based on 352 patients who had been diagnosed...... mediated through either optimal surgery or chemotherapy. Taken together, about 26% of the beneficial effect of research activity was mediated through the proposed pathways. CONCLUSIONS: Mediation analysis allows proceeding from the question "Does it work?" to the question "How does it work?" In particular...

Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

KAUST Repository

Migliorati, Giovanni

2016-01-01

We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

Science.gov (United States)

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

International Nuclear Information System (INIS)

Yu, P.

2008-01-01

More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

Science.gov (United States)

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Joint survival probability via truncated invariant copula

International Nuclear Information System (INIS)

Kim, Jeong-Hoon; Ma, Yong-Ki; Park, Chan Yeol

2016-01-01

Highlights: • We have studied an issue of dependence structure between default intensities. • We use a multivariate shot noise intensity process, where jumps occur simultaneously and their sizes are correlated. • We obtain the joint survival probability of the integrated intensities by using a copula. • We apply our theoretical result to pricing basket default swap spread. - Abstract: Given an intensity-based credit risk model, this paper studies dependence structure between default intensities. To model this structure, we use a multivariate shot noise intensity process, where jumps occur simultaneously and their sizes are correlated. Through very lengthy algebra, we obtain explicitly the joint survival probability of the integrated intensities by using the truncated invariant Farlie–Gumbel–Morgenstern copula with exponential marginal distributions. We also apply our theoretical result to pricing basket default swap spreads. This result can provide a useful guide for credit risk management.

Ischemic risk stratification by means of multivariate analysis of the heart rate variability

International Nuclear Information System (INIS)

Valencia, José F; Vallverdú, Montserrat; Caminal, Pere; Porta, Alberto; Voss, Andreas; Schroeder, Rico; Vázquez, Rafael; Bayés de Luna, Antonio

2013-01-01

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures. (paper)

18F-Fluoride PET/CT tumor burden quantification predicts survival in breast cancer.

Science.gov (United States)

Brito, Ana E; Santos, Allan; Sasse, André Deeke; Cabello, Cesar; Oliveira, Paulo; Mosci, Camila; Souza, Tiago; Amorim, Barbara; Lima, Mariana; Ramos, Celso D; Etchebehere, Elba

2017-05-30

In bone-metastatic breast cancer patients, there are no current imaging biomarkers to identify which patients have worst prognosis. The purpose of our study was to investigate if skeletal tumor burden determined by 18F-Fluoride PET/CT correlates with clinical outcomes and may help define prognosis throughout the course of the disease. Bone metastases were present in 49 patients. On multivariable analysis, skeletal tumor burden was significantly and independently associated with overall survival (p breast cancer patients (40 for primary staging and the remainder for restaging after therapy). Clinical parameters, primary tumor characteristics and skeletal tumor burden were correlated to overall survival, progression free-survival and time to bone event. The median follow-up time was 19.5 months. 18F-Fluoride PET/CT skeletal tumor burden is a strong independent prognostic imaging biomarker in breast cancer patients.

Multivariate analysis of microarray data: differential expression and differential connection.

Science.gov (United States)

Kiiveri, Harri T

2011-02-01

Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...

LEF-1 and TCF4 expression correlate inversely with survival in colorectal cancer

Directory of Open Access Journals (Sweden)

Kirchner Thomas

2010-11-01

Full Text Available Abstract Background Most colorectal carcinomas are driven by an activation of the canonical Wnt signalling pathway, which promotes the expression of multiple target genes mediating proliferation inavasion and invasion. Upon activation of the Wnt signalling pathway its key player β-catenin translocates from the cytoplasm to the nucleus and binds to members of the T-cell factor (TCF/lymphoid enhancer factor (LEF-1 family namely LEF-1 and TCF4 which are central mediators of transcription. In this study we investigated the expression of β-Catenin, LEF1 and TCF4 in colorectal carcinomas and their prognostic significance. Methods Immunohistochemical analyses of LEF-1, TCF4 and nuclear β-Catenin were done using a tissue microarray with 214 colorectal cancer specimens. The expression patterns were compared with each other and the results were correlated with clinicopathologic variables and overall survival in univariate and multivariate analysis. Results LEF-1 expression was found in 56 (26% and TCF4 expression in 99 (46% of colorectal carcinomas and both were heterogenously distributed throughout the tumours. Comparing LEF-1, TCF4 and β-catenin expression patterns we found no correlation. In univariate analysis, TCF4 expression turned out to be a negative prognostic factor being associated with shorter overall survival (p = 0.020, whereas LEF-1 expression as well as a LEF-1/TCF4 ratio were positive prognostic factors and correlated with longer overall survival (p = 0.015 respectively p = 0.001. In multivariate analysis, LEF-1 and TCF4 expression were confirmed to be independent predictors of longer respectively shorter overall survival, when considered together with tumour stage, gender and age (risk ratio for LEF-1: 2.66; p = 0.027 risk ratio for TCF4: 2.18; p = 0.014. Conclusions This study demonstrates different prognostic values of LEF-1 and TCF4 expression in colorectal cancer patients indicating different regulation of these transcription

Improved survival for rectal cancer compared to colon cancer: the four cohort study.

Science.gov (United States)

Buchwald, Pamela; Hall, Claire; Davidson, Callum; Dixon, Liane; Dobbs, Bruce; Robinson, Bridget; Frizelle, Frank

2018-03-01

Colorectal cancer (CRC) is the third most common cancer worldwide. This study was undertaken to evaluate survival outcomes and changes of disease outcomes of CRC patients over the last decades. A retrospective analysis of CRC patients in Christchurch was performed in four patient cohorts at 5 yearly intervals; 1993-94, 1998-99, 2004-05 and 2009. Data on cancer location, stage, surgical and oncological treatment and survival were collected. Univariate, multivariate and Kaplan-Meier survival analysis were performed. There were 1391 patients (355, 317, 419 and 300 per cohort), 1037 colon and 354 rectal cancers, respectively. For colon cancer, right-sided cancers appeared more common in later cohorts (P = 0.01). There was a significant decrease in the number of permanent stomas for colon cancer patients (P = 0.001). There was an analogous trend for rectal cancers (P = 0.075). More CRC patients with stage IV disease were treated surgically (P = 0.001) and colon cancer stages I and II tended to have increased survival if operated by a colorectal surgeon (P = 0.06). Oncology referrals have increased remarkably (P = 0.001). Overall 56% of patients were alive at 5 years however rectal cancer patients had significantly better 5-year survival than those with colon cancer (P rectal cancer patients have a better 5-year survival than colon cancer patients. The improved survival with early stage colon cancers operated on by specialist colorectal surgeons needs further exploration. © 2016 Royal Australasian College of Surgeons.

[Survival rate for breast cancer in Rabat (Morocco) 2005-2008].

Science.gov (United States)

Mechita, Nada Bennani; Tazi, Mohammed Adnane; Er-Raki, Abdelouahed; Mrabet, Mustapha; Saadi, Asma; Benjaafar, Noureddine; Razine, Rachid

2016-01-01

Breast cancer is a public health problem in Morocco. This study aims to estimate the survival rate for patients with breast cancer living in Rabat. We conducted a prognostic study of female patients with breast cancer diagnosed during 2005-2008, living in Rabat and whose data were recorded in the Rabat Cancer Registry. The date of inclusion in this study corresponded with the date on which cancer was histologically confirmed. Survival rate was estimated using the Kaplan-Meier method and the comparison between the different classes of a variable was made using the log rank test. The study of factors associated with survival was performed using the Cox model. During the study period 628 cases of breast cancer were collected. Mortality rate was 19.9%. Overall 1-year survival rate was 97.1%, 89.2% at 3 years and 80.6% at 5 years. In multivariate analysis, breast cancer survival was statistically lower in patients over 70 years of age (p <0.001) with large tumor size (p < 0.001), advanced-stage adenopathies (p = 0.007), metastases (p < 0.001) and not using hormone therapy (p = 0.002). Large tumor size and metastases are poor prognostic factors in breast cancer, hence the need to strengthen screening programs.

Multivariate pattern dependence.

Directory of Open Access Journals (Sweden)

Stefano Anzellotti

2017-11-01

Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

[Medulloblastoma: improved survival in recent decades. Unicentric experience].

Science.gov (United States)

Igual Estellés, Lucía; Berlanga Charriel, Pablo; Cañete Nieto, Adela

2017-01-01

The aim of the study is to analyse variations in the treatment of medulloblastoma, the most common childhood brain tumour, and its impact on survival over the past two decades, as well as its clinical and pathological features. Survival analysis of all patients under 14 years old diagnosed with medulloblastoma between January 1990 and December 2013 in a Paediatric Oncology Unit. Sixty-three patients were diagnosed and treated for medulloblastoma, with a median follow-up of 5.1 years (range 0.65-21.7 years). The overall survival (OS) at 3 and 5 years was 66±13% and 55±14%, respectively. The OS at 5 years was 44%±25% in patients diagnosed in the 1990's, showing an increase to 70%±23% (p=0.032) since 2000. Clinical prognosis factors were included in the logistic regression model: age (p=0.008), presence of metastases and/or residual tumour (p=0.007), and receiving chemotherapy with radiotherapy after surgery (p=0.008). Statistically significant differences were observed for all of them. In our institution there has been a significant increase in medulloblastoma survival in the last decades. Multivariate analysis showed that this improvement was not related to the date of diagnosis, but with the introduction of chemotherapy in adjuvant treatment. This study confirmed that clinical factors significantly associated with worse outcome were age and presence of metastases at diagnosis. Copyright © 2016 Asociación Española de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.

Does biological relatedness affect child survival?

Directory of Open Access Journals (Sweden)

2003-05-01

Full Text Available Objective: We studied child survival in Rakai, Uganda where many children are fostered out or orphaned. Methods: Biological relatedness is measured as the average of the Wright's coefficients between each household member and the child. Instrumental variables for fostering include proportion of adult males in household, age and gender of household head. Control variables include SES, religion, polygyny, household size, child age, child birth size, and child HIV status. Results: Presence of both parents in the household increased the odds of survival by 28%. After controlling for the endogeneity of child placement decisions in a multivariate model we found that lower biological relatedness of a child was associated with statistically significant reductions in child survival. The effects of biological relatedness on child survival tend to be stronger for both HIV- and HIV+ children of HIV+ mothers. Conclusions: Reductions in the numbers of close relatives caring for children of HIV+ mothers reduce child survival.

Causal inference in survival analysis using pseudo-observations

DEFF Research Database (Denmark)

Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

2017-01-01

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...

Predicting survival for well-differentiated liposarcoma: the importance of tumor location.

Science.gov (United States)

Smith, Caitlin A; Martinez, Steve R; Tseng, Warren H; Tamurian, Robert M; Bold, Richard J; Borys, Dariusz; Canter, Robert J

2012-06-01

Although well-differentiated liposarcoma (WD Lipo) is a low grade neoplasm with a negligible risk of metastatic disease, it can be locally aggressive. We hypothesized that survival for WD Lipo varies significantly based on tumor location. We identified 1266 patients with WD Lipo in the Surveillance, Epidemiology, and End Results database from 1988-2004. After excluding patients diagnosed by autopsy only, those lacking histologic confirmation, those lacking data on tumor location, and those with metastatic disease or unknown staging information, we arrived at a final study cohort of 1130 patients. Clinical, pathologic, and treatment variables were analyzed for their association with overall survival (OS) and disease-specific survival (DSS) using Kaplan-Meier analysis and Cox proportional hazards multivariate models. Mean age was 61 y (± 14.6), 72.2% were white, and 60.4% were male. Eighty-one percent of patients were treated with surgical therapy alone, 4.6% were treated with radiotherapy (RT) alone, and 12.9% were treated with both surgery and RT. Extremity location was most common (41.6%), followed by trunk (29%), retroperitoneal/intra-abdominal (RIA, 21.6%), thorax (4.2%), and head/neck (3.6%). With a median follow-up of 45 mo, median OS was 115 mo (95% confidence interval [CI] 92-138 mo) for RIA tumors compared to not reached for other tumor locations (P = 0.002). On multivariate analysis, increasing age and RIA location both predicted worse OS and DSS while tumor size, race, sex, receipt of RT, and Surveillance, Epidemiology, and End Results (SEER) stage did not. Tumor size became a significant predictor of worse DSS, but not OS, only when site, SEER stage, and extent of resection were removed from the multivariate model. Non-RIA locations, including extremity, experienced statistically similar OS, but 5-y DSS for trunk location was intermediate [92.3%, (95% CI 88.5%-96.1%) compared with 98.0% (95% CI, 96.2%-99.8%) for extremity and 86.6 (95% CI 81

Survival Analysis of Patients with End Stage Renal Disease

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

2015-06-01

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

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

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Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

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Isabel Gallego-Alvarez

2014-11-01

Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan

International Nuclear Information System (INIS)

Adam, Abdel Majid A.; Eltayeb, Mohamed Ahmed H.

2012-01-01

Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q–Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate. - Highlights: ► Multivariate statistical techniques were used to characterize radioactive data. ► U-238 in Uro phosphate shows higher degree of mobility (CV% = 82.6). ► U-238 in Kurun phosphate shows lower degree of mobility (CV% = 64.7). ► The radioactive variables did not follow a normal distribution. ► The ratio of Th

The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

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Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

2017-02-01

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

An uncertain journey around the tails of multivariate hydrological distributions

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Serinaldi, Francesco

2013-10-01

Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

Decreased survival in patients with carcinoma of axillary tail versus upper outer quadrant breast cancers: a SEER population-based study

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Gou ZC

2018-05-01

Full Text Available Zong-Chao Gou,1,2,* Xi-Yu Liu,1,2,* Yi Xiao,1,2 Shen Zhao,1,2 Yi-Zhou Jiang,1,2 Zhi-Ming Shao1–3 1Department of Breast Surgery, Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People’s Republic of China; 3Institutes of Biomedical Sciences, Fudan University, Shanghai, People’s Republic of China *These authors contributed equally to this work Background: Carcinoma of the axillary tail of Spence (CATS is a poorly studied type of breast cancer. The clinicopathological characteristics and prognostic features of CATS are unclear. Methods: Using the Surveillance, Epidemiology, and End Results database, we identified 149,026 patients diagnosed with upper outer quadrant breast cancer (UOBC (n=146,343 or CATS (n=2,683. The median follow-up was 88 months. The primary and secondary outcomes were breast cancer-specific survival (BCSS and overall survival. The survival outcomes of UOBC and CATS were compared using competing risks analysis, log-rank test, Cox proportional hazards regression model, and propensity score matching method. Multivariate logistic regression was utilized to present the relationship between CATS and lymph node (LN metastasis. Results: CATS presented a higher grade, higher negative hormone receptor rate, and more positive nodal metastasis. The 10-year BCSS rate was worse for CATS than for UOBC (85.1% vs 87.3%, P=0.001. The multivariate Cox analysis showed a higher hazard ratio (HR for CATS over UOBC (BCSS: HR =1.20, P=0.001; overall survival: HR =1.11, P=0.019. The difference in the BCSS was also observed in a 1:1 matched cohort (BCSS P=0.019. A subgroup analysis revealed the inferior outcomes of CATS in the metastatic LN subgroup and the hormone receptor-negative subgroup. The multivariate logistic regression indicated that CATS is an independent contributing factor to LN metastasis. Conclusion: CATS

Multivariate analyses to assess the effects of surgeon and hospital volume on cancer survival rates: a nationwide population-based study in Taiwan.

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Chun-Ming Chang

Full Text Available BACKGROUND: Positive results between caseloads and outcomes have been validated in several procedures and cancer treatments. However, there is limited information available on the combined effects of surgeon and hospital caseloads. We used nationwide population-based data to explore the association between surgeon and hospital caseloads and survival rates for major cancers. METHODOLOGY: A total of 11,677 patients with incident cancer diagnosed in 2002 were identified from the Taiwan National Health Insurance Research Database. Survival analysis, the Cox proportional hazards model, and propensity scores were used to assess the relationship between 5-year survival rates and different caseload combinations. RESULTS: Based on the Cox proportional hazard model, cancer patients treated by low-volume surgeons in low-volume hospitals had poorer survival rates, and hazard ratios ranged from 1.3 in head and neck cancer to 1.8 in lung cancer after adjusting for patients' demographic variables, co-morbidities, and treatment modality. When analyzed using the propensity scores, the adjusted 5-year survival rates were poorer for patients treated by low-volume surgeons in low-volume hospitals, compared to those treated by high-volume surgeons in high-volume hospitals (P<0.005. CONCLUSIONS: After adjusting for differences in the case mix, cancer patients treated by low-volume surgeons in low-volume hospitals had poorer 5-year survival rates. Payers may implement quality care improvement in low-volume surgeons.

Novel Inflammation-Based Prognostic Score for Predicting Survival in Patients with Metastatic Urothelial Carcinoma.

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Yu-Li Su

Full Text Available We developed a novel inflammation-based model (NPS, which consisted of a neutrophil to lymphocyte ratio (NLR and platelet count (PC, for assessing the prognostic role in patients with metastatic urothelial carcinoma (UC.We performed a retrospective analysis of patients with metastatic UC who underwent systemic chemotherapy between January 1997 and December 2014 in Kaohsiung Chang Gung Memorial Hospital. The defined cutoff values for the NLR and PC were 3.0 and 400 × 103/μL, respectively. Patients were scored 1 for either an elevated NLR or PC, and 0 otherwise. The NPS was calculated by summing the scores, ranging from 0 to 2. The primary endpoint was overall survival (OS by using Kaplan-Meier analysis. Multivariate Cox regression analysis was used to identify the independent prognostic factors for OS.In total, 256 metastatic UC patients were enrolled. Univariate analysis revealed that patients with either a high NLR or PC had a significantly shorter survival rate compared with those with a low NLR (P = .001 or PC (P < .0001. The median OS in patients with NPS 0, 1, and 2 was 19.0, 12.8, and 9.3 months, respectively (P < .0001. Multivariate analysis revealed that NPS, along with the histologic variant, liver metastasis, age, and white cell count, was an independent factor facilitating OS prediction (hazard ratio 1.64, 95% confidence interval 1.20-2.24, P = .002.The NLR and PC are independent prognostic factors for OS in patients with metastatic UC. The NPS model has excellent discriminant ability for OS.

The Influence of Cyst Emptying, Lymph Node Resection and Chemotherapy on Survival in Stage IA and IC1 Epithelial Ovarian Cancer

DEFF Research Database (Denmark)

Rosendahl, Mikkel; Mosgaard, Berit Jul; Høgdall, Claus

2016-01-01

AIM: To determine if survival in stage I ovarian cancer is influenced by cyst emptying, lymph node resection and chemotherapy. PATIENTS AND METHODS: A survival analysis of 607 patients with ovarian cancer in stage IA, IA with cyst emptying (IAempty) and IC1 was performed. RESULTS......: There was no difference in five-year survival between IA (87%) and IC1 (87%) (p=0.899), between IA and IAempty (86%) (p=0.500) nor between IA+IAempty (87%) and IC1 without IAempty (84%) (p=0.527). Five-year survival rate (5YSR) was significantly higher after lymph node resection in stage IA (94% vs. 85%; p=0.01) and IA......+IC1 (93% vs. 85%; p=0.004). In multivariate analysis, lymph node resection improved prognosis significantly for all sub-stages, whereas stage and chemotherapy did not affect survival. CONCLUSION: In stage IA ovarian cancer, controlled cyst emptying without spill does not worsen prognosis. Lymph node...

Overall survival and disease-free survival in endometrial cancer: prognostic factors in 276 patients

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Tejerizo-García A

2013-09-01

Full Text Available Álvaro Tejerizo-García,1 Jesús S Jiménez-López,1 José L Muñoz-González,1 Sara Bartolomé-Sotillos,1 Laura Marqueta-Marqués,1 Gregorio López-González,1 José F Pérez-Regadera Gómez21Service of Obstetrics and Gynecology, 2Radiation Oncology Service, Hospital Universitario 12 de Octubre, Madrid, SpainObjective: The aim of the study reported here was to assess the disease-free survival and overall survival of patients with endometrial cancer and to determine independent factors affecting the prognosis.Materials and methods: This was a retrospective study of a single-center clinical series of 276 patients (mean age 64 years with histologically confirmed cancer of the corpus uteri. The standard treatments were extrafascial total hysterectomy and bilateral salpingo-oophorectomy with selective pelvic/para-aortic node dissection, according to risk for recurrence. Actuarial overall survival and disease-free survival were estimated according to the Kaplan–Meier method. Univariate and multivariate Cox proportional hazards analyses were used to assess the prognostic significance of the different variables.Results: The estimated median follow-up, determined using the inverse Kaplan–Meier method, was 45 months (95% confidence interval [CI] 41.2–48.8 for disease-free survival and 46 months (95% CI 43.0–49.0 for overall survival. The statistically significant variables affecting disease-free survival and overall survival were age, serous-papillary and clear-cell histological types, outer-half myometrial invasion, advanced International Federation of Gynecology and Obstetrics (FIGO stage, tumor grades G2 and G3, incomplete surgical resection, positive lymph nodes, lymphovascular space invasion, tumor remnants of >1 cm after surgery, and high-risk group. In the multivariate Cox regression model, predictors of tumor recurrence included advanced FIGO stage (hazard ratio [HR] 4.90, 95% CI 2.57–9.36, P < 0.001 and tumor grades G2 (HR 4.79, 95

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

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Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

Tamoxifen therapy improves overall survival in luminal A subtype of ductal carcinoma in situ: a study based on nationwide Korean Breast Cancer Registry database.

Science.gov (United States)

Hwang, Ki-Tae; Kim, Eun-Kyu; Jung, Sung Hoo; Lee, Eun Sook; Kim, Seung Il; Lee, Seokwon; Park, Heung Kyu; Kim, Jongjin; Oh, Sohee; Kim, Young A

2018-06-01

To determine the prognostic role of tamoxifen therapy for patients with ductal carcinoma in situ (DCIS) according to molecular subtypes. Data of 14,944 patients with DCIS were analyzed. Molecular subtypes were classified into four categories based on expression of estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Kaplan-Meier estimator was used for overall survival analysis while Cox proportional hazards model was used for univariate and multivariate analyses. Luminal A subtype (ER/PR+, HER2-) showed higher (P = .009) survival rate than triple-negative (TN) subtype. Tamoxifen therapy group showed superior (P < .001) survival than no-tamoxifen therapy group. It had survival benefit only for luminal A subtype (P = .001). Tamoxifen therapy resulted in higher survival rate in subgroups with positive ER (P = .006), positive PR (P = .009), and negative HER2 (P < .001). In luminal A subtype, tamoxifen therapy showed lower hazard ratio (HR) compared to no-tamoxifen therapy (HR, 0.420; 95% CI 0.250-0.705; P = .001). Tamoxifen therapy was a significant independent factor by multivariate analysis (HR, 0.538; 95% CI 0.306-0.946; P = .031) as well as univariate analysis. Tamoxifen therapy group showed superior prognosis than the no-tamoxifen therapy group. Its prognostic influence was only effective for luminal A subtype. Patients with luminal A subtype showed higher survival rate than those with TN subtype. Active tamoxifen therapy is recommended for DCIS patients with luminal A subtype, and routine tests for ER, PR, and HER2 should be considered for DCIS.