Statistical Methods For Survival Data Analysis
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Statistical Methods For Survival Data Analysis
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Author : Elisa T. Lee
language : en
Publisher: John Wiley & Sons
Release Date : 2013-09-23
Statistical Methods For Survival Data Analysis written by Elisa T. Lee and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-23 with Mathematics categories.
Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences. Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes: Marginal and random effect models for analyzing correlated censored or uncensored data Multiple types of two-sample and K-sample comparison analysis Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of the presented material Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
Handbook Of Survival Analysis
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Author : John P. Klein
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Handbook Of Survival Analysis written by John P. Klein and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Statistical Methods For Survival Data Analysis
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Author : Elisa T. Lee
language : en
Publisher: Wiley-Interscience
Release Date : 2003-07-21
Statistical Methods For Survival Data Analysis written by Elisa T. Lee and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-21 with Mathematics categories.
Third Edition brings the text up to date with new material and updated references. New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model. Coverage of graphical methods has been deleted. Large data sets are provided on an FTP site for readers' convenience. Bibliographic remarks conclude each chapter.
Statistical Methods On Survival Data With Measurement Error
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Author : Ying Yan
language : en
Publisher:
Release Date : 2014
Statistical Methods On Survival Data With Measurement Error written by Ying Yan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
In survival data analysis, covariates are often subject to measurement error. A naive analysis with measurement error ignored commonly leads to biased estimation of parameters of survival models. Measurement error also causes efficiency loss for detecting possible association between risk factors and time to event. Furthermore, it induces difficulty on model building and model checking, because the presence of measurement error frequently masks true underlying patterns of data. Although there has been a large body of literature to handle error-prone survival data since the paper by Prentice (1982), many important issues still remain unexplored in this area. This thesis focuses on several important issues of survival analysis with covariate measurement error. One problem that has received little attention is on misspecification of measurement error models. In this thesis, we investigate this important problem with the attention particularly paid to error-contaminated survival data under the Cox model. In particular, we conduct bias analysis which offers a way to unify many existing methods of survival data with measurement error, and study the impact of misspecifying the error models in survival data analysis. A simple expression is obtained to quantify the bias of "working" estimators derived under misspecified error models. Consistent estimators under general error models are derived based on this simple expression. Furthermore, we study hypothesis testing with both model misspecification and measurement error present. A second problem of our interest is about the validity of survival model assumptions when measurement error is involved. In the literature, a large number of methods have been developed to correct for measurement error effects, and these methods basically assume the survival model to be the Cox model. When the Cox model or the error model assumptions fail to hold, existing methods would break down. In this thesis, we address the issue of checking the Cox model assumptions with measurement error. We propose valid goodness of fit tests for survival data with covariate measurement error. This research offers us an important addition to the literature of survival data with measurement error. Our third topic concerns survival data analysis under additive hazards models with covariate measurement error. The additive hazards model is a useful and important alternative to the Cox model. However, this model is relatively less studied for situations where covariates are measured with error. In this thesis, we make important contributions to this topic. Specifically, we explore asymptotic bias induced from ignoring measurement error. A number of inference methods are developed to correct for error effects. The validity of the proposed methods is justified both theoretically and empirically. We investigate issues of model checking and model misspecification as well. In many studies, collection of data often includes a large number of variables in which many of them are unimportant in explaining survival of an individual. An important task is thus to identify relevant risk factors which are linked to the hazards of subjects. Although there is work on variable selection for survival data analysis, the available methods typically require all variables be precisely measured. This requirement is, however, often infeasible. More challengingly, in some studies, the dimension of the risk factors can be quite large or even much larger than the size of subjects. Our fourth topic concerns about estimation and variable selection for survival data with high dimensional mismeasured covariates. We propose corrected penalized methods. Our methods can adjust for measurement error effects, and perform estimation and variable selection simultaneously. Our research on this topic closes multiple gaps among the areas of survival analysis, measurement error and variable selection.
Statistical Methods For Survival Data Analysis
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Author : Elisa T. Lee
language : en
Publisher: Wiley-Interscience
Release Date : 1992-05-07
Statistical Methods For Survival Data Analysis written by Elisa T. Lee and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-05-07 with Mathematics categories.
Functions of survival time; Examples of survival data analysis; Nonparametric methods of estimating survival functions; Nonparametric methods for comparing survival distributions; Some well-known survival distributions and their applications; Graphical methods for sulvival distribution fitting and goodness-of-fit tests; Analytical estimation procedures for sulvival distributions; Parametric methods for comparing two survival distribution; Identification of prognostic factors related to survival time; Identification of risk factors related to dichotomous data; Planning and design of clinical trials (I); Planning and design of clinicL trials(II).
Survival Analysis
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Author : Alejandro Quiroz Flores
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-26
Survival Analysis written by Alejandro Quiroz Flores and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Political Science categories.
Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.
Analysis Of Survival Data
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Author : D.R. Cox
language : en
Publisher: Routledge
Release Date : 2018-02-19
Analysis Of Survival Data written by D.R. Cox and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-19 with Mathematics categories.
This monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom from symptoms of a disease as related to a treatment applied individual histories and so on, is obvious. The techniques also find important applications in industrial life testing and a range of subjects from physics to econometrics. In the eleven chapters of the book the methods and applications of are discussed and illustrated by examples.
Survival Analysis
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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher: CRC Press
Release Date : 2022-08-26
Survival Analysis written by Prabhanjan Narayanachar Tattar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-26 with Computers categories.
Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book
Statistical Methods For Survival Data Analysis
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Author :
language : en
Publisher:
Release Date : 1998
Statistical Methods For Survival Data Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Statistical Modelling Of Survival Data With Random Effects
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Author : Il Do Ha
language : en
Publisher: Springer
Release Date : 2018-01-02
Statistical Modelling Of Survival Data With Random Effects written by Il Do Ha and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-02 with Mathematics categories.
This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.