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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
  • Publsiher : Academic Press
  • Release : 06 October 2016
  • ISBN : 0081010087
  • Pages : 102 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKAnalysis for Time to Event Data under Censoring and Truncation

Summary:
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


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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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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Advanced Survival Models

Advanced Survival Models
  • Author : Catherine Legrand
  • Publisher : CRC Press
  • Release : 23 March 2021
GET THIS BOOKAdvanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when

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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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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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Flexible Imputation of Missing Data, Second Edition

Flexible Imputation of Missing Data, Second Edition
  • Author : Stef van Buuren
  • Publisher : CRC Press
  • Release : 17 July 2018
GET THIS BOOKFlexible Imputation of Missing Data, Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation

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Epidemiology and Medical Statistics

Epidemiology and Medical Statistics
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 21 November 2007
GET THIS BOOKEpidemiology and Medical Statistics

This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the

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Flexible Imputation of Missing Data, Second Edition

Flexible Imputation of Missing Data, Second Edition
  • Author : Stef van Buuren
  • Publisher : CRC Press
  • Release : 17 July 2018
GET THIS BOOKFlexible Imputation of Missing Data, Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation

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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.

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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

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Revenue Management and Survival Analysis in the Automobile Industry

Revenue Management and Survival Analysis in the Automobile Industry
  • Author : André Jerenz
  • Publisher : Springer Science & Business Media
  • Release : 01 August 2008
GET THIS BOOKRevenue Management and Survival Analysis in the Automobile Industry

André Jerenz develops a price-based revenue management framework to support retailers in establishing better and more profitable pricing strategies, including assigning an initial asking price and the adjustment of price over time.

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Handbook of Infectious Disease Data Analysis

Handbook of Infectious Disease Data Analysis
  • Author : Leonhard Held,Niel Hens,Philip D O'Neill,Jacco Wallinga
  • Publisher : CRC Press
  • Release : 04 November 2019
GET THIS BOOKHandbook of Infectious Disease Data Analysis

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the

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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

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