Analysis for Time to Event Data under Censoring and Truncation

Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data

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  • Author : Hongsheng Dai
  • Publisher : Academic Press
  • Pages : 102 pages
  • ISBN : 0081010087
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAnalysis for Time to Event Data under Censoring and Truncation

Analysis for Time-to-Event Data under Censoring and Truncation

Analysis for Time-to-Event Data under Censoring and Truncation
  • Author : Hongsheng Dai,Huan Wang
  • Publisher : Academic Press
  • Release : 06 October 2016
GET THIS BOOKAnalysis for Time-to-Event Data under Censoring and Truncation

Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents

Survival Analysis

Survival Analysis
  • Author : John P. Klein,Melvin L. Moeschberger
  • Publisher : Springer Science & Business Media
  • Release : 29 June 2013
GET THIS BOOKSurvival Analysis

Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to

Survival Analysis Using S

Survival Analysis Using S
  • Author : Mara Tableman,Jong Sung Kim
  • Publisher : CRC Press
  • Release : 28 July 2003
GET THIS BOOKSurvival Analysis Using S

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.

Interval-Censored Time-to-Event Data

Interval-Censored Time-to-Event Data
  • Author : Ding-Geng (Din) Chen,Jianguo Sun,Karl E. Peace
  • Publisher : CRC Press
  • Release : 19 July 2012
GET THIS BOOKInterval-Censored Time-to-Event Data

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current

Survival Analysis: State of the Art

Survival Analysis: State of the Art
  • Author : John P. Klein,Prem Goel
  • Publisher : Springer Science & Business Media
  • Release : 29 February 1992
GET THIS BOOKSurvival Analysis: State of the Art

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

Analysis of Doubly Truncated Data

Analysis of Doubly Truncated Data
  • Author : Achim Dörre,Takeshi Emura
  • Publisher : Springer
  • Release : 13 May 2019
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This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes

Applied Survival Analysis

Applied Survival Analysis
  • Author : David W. Hosmer, Jr.,Stanley Lemeshow,Susanne May
  • Publisher : John Wiley & Sons
  • Release : 23 September 2011
GET THIS BOOKApplied Survival Analysis

THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study.

Introducing Survival and Event History Analysis

Introducing Survival and Event History Analysis
  • Author : Melinda Mills
  • Publisher : SAGE
  • Release : 19 January 2011
GET THIS BOOKIntroducing Survival and Event History Analysis

This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read,

Analysis of Survival Data with Dependent Censoring

Analysis of Survival Data with Dependent Censoring
  • Author : Takeshi Emura,Yi-Hau Chen
  • Publisher : Springer
  • Release : 05 April 2018
GET THIS BOOKAnalysis of Survival Data with Dependent Censoring

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

Statistical Models Based on Counting Processes

Statistical Models Based on Counting Processes
  • Author : Per K. Andersen,Ornulf Borgan,Richard D. Gill,Niels Keiding
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKStatistical Models Based on Counting Processes

Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for

Reliability and Survival Analysis

Reliability and Survival Analysis
  • Author : Md. Rezaul Karim,M. Ataharul Islam
  • Publisher : Springer
  • Release : 09 August 2019
GET THIS BOOKReliability and Survival Analysis

This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit

Statistical Models and Methods for Lifetime Data

Statistical Models and Methods for Lifetime Data
  • Author : Jerald F. Lawless
  • Publisher : John Wiley & Sons
  • Release : 25 January 2011
GET THIS BOOKStatistical Models and Methods for Lifetime Data

Praise for the First Edition "An indispensable addition to any serious collection on lifetimedata analysis and . . . a valuable contribution to the statisticalliterature. Highly recommended . . ." -Choice "This is an important book, which will appeal to statisticiansworking on survival analysis problems." -Biometrics "A thorough, unified treatment of statistical models and methodsused in the analysis of lifetime data . . . this is a highlycompetent and agreeable statistical textbook." -Statistics in Medicine The statistical analysis of lifetime or response time data is a keytool in engineering,

An Introduction to Survival Analysis Using Stata, Second Edition

An Introduction to Survival Analysis Using Stata, Second Edition
  • Author : Mario Cleves,William Gould,William W. Gould,Roberto Gutierrez,Yulia Marchenko
  • Publisher : Stata Press
  • Release : 15 May 2008
GET THIS BOOKAn Introduction to Survival Analysis Using Stata, Second Edition

"[This book] provides new researchers with the foundation for understanding the various approaches for analyzing time-to-event data. This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.

The Statistical Analysis of Failure Time Data

The Statistical Analysis of Failure Time Data
  • Author : John D. Kalbfleisch,Ross L. Prentice
  • Publisher : John Wiley & Sons
  • Release : 25 January 2011
GET THIS BOOKThe Statistical Analysis of Failure Time Data

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.

Survival Analysis

Survival Analysis
  • Author : David Machin,Yin Bun Cheung,Mahesh Parmar
  • Publisher : John Wiley & Sons
  • Release : 30 March 2006
GET THIS BOOKSurvival Analysis

Well received in its first edition, Survival Analysis: A Practical Approach is completely revised to provide an accessible and practical guide to survival analysis techniques in diverse environments. Illustrated with many authentic examples, the book introduces basic statistical concepts and methods to construct survival curves, later developing them to encompass more specialised and complex models. During the years since the first edition there have been several new topics that have come to the fore and many new applications. Parallel developments