The Statistical Analysis Of Multivariate Failure Time Data

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The Statistical Analysis of Multivariate Failure Time Data

Author : Ross L. Prentice,Shanshan Zhao
Publisher : CRC Press
Page : 224 pages
File Size : 49,9 Mb
Release : 2019-05-14
Category : Mathematics
ISBN : 9781482256581

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The Statistical Analysis of Multivariate Failure Time Data by Ross L. Prentice,Shanshan Zhao Pdf

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

The Statistical Analysis of Failure Time Data

Author : John D. Kalbfleisch,Ross L. Prentice
Publisher : John Wiley & Sons
Page : 462 pages
File Size : 55,9 Mb
Release : 2011-01-25
Category : Mathematics
ISBN : 9781118031230

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The Statistical Analysis of Failure Time Data by John D. Kalbfleisch,Ross L. Prentice Pdf

Contains additional discussion and examples on left truncationas well as material on more general censoring and truncationpatterns. Introduces the martingale and counting process formulation swillbe in a new chapter. Develops multivariate failure time data in a separate chapterand extends the material on Markov and semi Markovformulations. Presents new examples and applications of data analysis.

The Statistical Analysis of Failure Time Data

Author : John D. Kalbfleisch,Ross L. Prentice
Publisher : Wiley-Interscience
Page : 344 pages
File Size : 46,5 Mb
Release : 1980
Category : Mathematics
ISBN : UOM:39015012442854

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The Statistical Analysis of Failure Time Data by John D. Kalbfleisch,Ross L. Prentice Pdf

Failure time models; Inference in parametric models and related topics; The proportional hazards model; Likelihood construction and further results on the proportional hazards model; Inference based on ranks in the accelerated failure time model; Multivariate failure time data and competing risks; Miscellaneous topics.

The Statistical Analysis of Multivariate Failure Time Data

Author : Ross L. Prentice,Shanshan Zhao
Publisher : CRC Press
Page : 110 pages
File Size : 49,6 Mb
Release : 2019-05-14
Category : Mathematics
ISBN : 9780429529702

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The Statistical Analysis of Multivariate Failure Time Data by Ross L. Prentice,Shanshan Zhao Pdf

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

The Statistical Analysis of Failure Time Data

Author : J. D. Kalbfleisch
Publisher : Unknown
Page : 128 pages
File Size : 40,7 Mb
Release : 1984
Category : Electronic
ISBN : OCLC:793326247

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The Statistical Analysis of Failure Time Data by J. D. Kalbfleisch Pdf

The Frailty Model

Author : Luc Duchateau,Paul Janssen
Publisher : Springer Science & Business Media
Page : 316 pages
File Size : 43,8 Mb
Release : 2007-10-23
Category : Mathematics
ISBN : 9780387728353

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The Frailty Model by Luc Duchateau,Paul Janssen Pdf

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

Survival Analysis: State of the Art

Author : John P. Klein,P.K. Goel
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 40,7 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9789401579834

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Survival Analysis: State of the Art by John P. Klein,P.K. Goel Pdf

Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.

The Statistical Analysis of Interval-censored Failure Time Data

Author : Jianguo Sun
Publisher : Springer
Page : 304 pages
File Size : 47,7 Mb
Release : 2007-05-26
Category : Mathematics
ISBN : 9780387371191

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The Statistical Analysis of Interval-censored Failure Time Data by Jianguo Sun Pdf

This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.

Analysis of Multivariate Survival Data

Author : Philip Hougaard
Publisher : Springer Science & Business Media
Page : 559 pages
File Size : 46,6 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461213048

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Analysis of Multivariate Survival Data by Philip Hougaard Pdf

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.

Statistical Analysis of Panel Count Data

Author : Jianguo Sun,Xingqiu Zhao
Publisher : Springer Science & Business Media
Page : 271 pages
File Size : 43,5 Mb
Release : 2013-10-09
Category : Medical
ISBN : 9781461487159

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Statistical Analysis of Panel Count Data by Jianguo Sun,Xingqiu Zhao Pdf

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.

Proceedings of the First Seattle Symposium in Biostatistics: Survival Analysis

Author : Danyu Lin,T.R. Fleming
Publisher : Springer Science & Business Media
Page : 314 pages
File Size : 48,6 Mb
Release : 2012-12-06
Category : Medical
ISBN : 9781468463163

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Proceedings of the First Seattle Symposium in Biostatistics: Survival Analysis by Danyu Lin,T.R. Fleming Pdf

The papers in this volume discuss important methodological advances in several important areas, including multivariate failure time data and interval censored data. The book will be an indispensable reference for researchers and practitioners in biostatistics, medical research, and the health sciences.

Omic Association Studies with R and Bioconductor

Author : Juan R. González,Alejandro Cáceres
Publisher : CRC Press
Page : 348 pages
File Size : 42,6 Mb
Release : 2019-06-14
Category : Mathematics
ISBN : 9780429803369

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Omic Association Studies with R and Bioconductor by Juan R. González,Alejandro Cáceres Pdf

After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions

Multivariate Survival Analysis and Competing Risks

Author : Martin J. Crowder
Publisher : CRC Press
Page : 402 pages
File Size : 42,6 Mb
Release : 2012-04-17
Category : Mathematics
ISBN : 9781439875223

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Multivariate Survival Analysis and Competing Risks by Martin J. Crowder Pdf

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate

Analysis of Failure and Survival Data

Author : Peter J. Smith
Publisher : CRC Press
Page : 258 pages
File Size : 45,9 Mb
Release : 2017-07-28
Category : Mathematics
ISBN : 9781351989671

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Analysis of Failure and Survival Data by Peter J. Smith Pdf

Analysis of Failure and Survival Data is an essential textbook for graduate-level students of survival analysis and reliability and a valuable reference for practitioners. It focuses on the many techniques that appear in popular software packages, including plotting product-limit survival curves, hazard plots, and probability plots in the context of censored data. The author integrates S-Plus and Minitab output throughout the text, along with a variety of real data sets so readers can see how the theory and methods are applied. He also incorporates exercises in each chapter that provide valuable problem-solving experience. In addition to all of this, the book also brings to light the most recent linear regression techniques. Most importantly, it includes a definitive account of the Buckley-James method for censored linear regression, found to be the best performing method when a Cox proportional hazards method is not appropriate. Applying the theories of survival analysis and reliability requires more background and experience than students typically receive at the undergraduate level. Mastering the contents of this book will help prepare students to begin performing research in survival analysis and reliability and provide seasoned practitioners with a deeper understanding of the field.

An Introduction to Applied Multivariate Analysis with R

Author : Brian Everitt,Torsten Hothorn
Publisher : Springer Science & Business Media
Page : 284 pages
File Size : 46,7 Mb
Release : 2011-04-23
Category : Mathematics
ISBN : 9781441996503

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An Introduction to Applied Multivariate Analysis with R by Brian Everitt,Torsten Hothorn Pdf

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.