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  • Release : 01 January 1970
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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 : 01 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

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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

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Survival Analysis

Survival Analysis
  • Author : John P. Klein,Melvin L. Moeschberger
  • Publisher : Springer Science & Business Media
  • Release : 17 May 2006
GET THIS BOOKSurvival Analysis

Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter

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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.

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Survival Analysis for Incomplete Data

Survival Analysis for Incomplete Data
  • Author : Yu-Ru Su
  • Publisher : Anonim
  • Release : 05 December 2021
GET THIS BOOKSurvival Analysis for Incomplete Data

There is a surge in medical follow-up studies that include longitudinal covariates in the modeling of survival data. So far, the focus has been largely on right censored survival data. In the first part of my dissertation, we consider survival data that are subject to both left truncation and right censoring. Left truncation is well known to produce biased sample. The sampling bias issue has been resolved in the literature for the case which involves baseline or time-varying covariates that

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Applied Survival Analysis Using R

Applied Survival Analysis Using R
  • Author : Dirk F. Moore
  • Publisher : Springer
  • Release : 11 May 2016
GET THIS BOOKApplied Survival Analysis Using R

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

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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

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Modern Statistical Methods for Complex Survival Data

Modern Statistical Methods for Complex Survival Data
  • Author : Jue Hou
  • Publisher : Anonim
  • Release : 05 December 2021
GET THIS BOOKModern Statistical Methods for Complex Survival Data

With the booming of big complex data, various Statistical methods and Data Science techniques have been developed to retrieve valuable information from them. The progress is slower with survival data due to the additional difficulty from censoring and truncation. Except for a few straightforward extensions, most modern learning methods have been absent in survival analysis for years since their invention. The theory on the survival version of those methods also falls further behind. There is a strong demand on computational

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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.

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Joint Models for Longitudinal and Time-to-Event Data

Joint Models for Longitudinal and Time-to-Event Data
  • Author : Dimitris Rizopoulos
  • Publisher : CRC Press
  • Release : 22 June 2012
GET THIS BOOKJoint Models for Longitudinal and Time-to-Event Data

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The

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The Statistical Analysis of Interval-censored Failure Time Data

The Statistical Analysis of Interval-censored Failure Time Data
  • Author : Jianguo Sun
  • Publisher : Springer
  • Release : 26 May 2007
GET THIS BOOKThe Statistical Analysis of Interval-censored Failure Time Data

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

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Analysis of Doubly Truncated Data

Analysis of Doubly Truncated Data
  • Author : Achim Dörre,Takeshi Emura
  • Publisher : Springer
  • Release : 13 May 2019
GET THIS BOOKAnalysis of Doubly Truncated Data

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

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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.

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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

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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,

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