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Analysis for Time-to-Event Data under Censoring and Truncation
1st Edition - September 26, 2016
Authors: Hongsheng Dai, Huan Wang
Language: English
Paperback ISBN:9780128054802
9 7 8 - 0 - 1 2 - 8 0 5 4 8 0 - 2
eBook ISBN:9780081010082
9 7 8 - 0 - 0 8 - 1 0 1 0 0 8 - 2
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 es…Read more
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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
Researchers and postgraduate students in mathematical statistics, applied statistics, or epidemiology
Chapter 1: Introduction
1.1 Introduction to the book
1.2 Examples
1.3 Brief review of survival analysis under truncation
1.4 Preliminaries
Chapter 2: Survival analysis for univariate truncated data
2.1 Introduction
2.2 Nonparametric estimation
2.3 Linear Rank Statistics for umbrella alternative hypothesis
2.4 Regression analysis for truncated and censored data
Chapter 3: Bivariate estimation with truncated survival data
3.1 Introduction
3.2 Bivariate distributions
3.3 Types of bivariate truncated survival data
3.4 The inverse probability weighted estimator with only one censoring variable
3.5 The transformation estimator
3.6 Example
3.7 Discussion
Chapter 4: Accelerated failure time model for truncated and censored survival data
4.1 Introduction
4.2 WLS estimator for univariate LTRC data under AFT model
4.3 AFT model for bivariate survival data under truncation and censoring
Chapter 5: Recent advances for truncated survival data
5.1 Linear transformation models
5.2 Joint modelling of survival events and longitudinal data under random truncation
Bibliography
Index
No. of pages: 102
Language: English
Edition: 1
Published: September 26, 2016
Imprint: Academic Press
Paperback ISBN: 9780128054802
eBook ISBN: 9780081010082
HD
Hongsheng Dai
Hongsheng Dai is a Lecturer in Statistics at the University of Essex, UK
Affiliations and expertise
Lecturer, University of Essex, UK.
HW
Huan Wang
Huan Wang is a Statistician and Epidemiologist, at the Dundee Epidemiology and Biostatistics Unit, Population Health Sciences, at the University of Dundee, UK. He received his BSc in Mathematics and Applied Mathematics, from the Harbin Institute of Technology, P.R China. He received his MSc in Applied Statistics from Lancaster University, U.K and completed his PhD in Mathematic Sciences at the University of Brighton, UK.
Affiliations and expertise
Statistician and Epidemiologist, Dundee Epidemiology and Biostatistics Unit, Population Health Sciences, University of Dundee, UK
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