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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
- No. of pages:
- © Academic Press 2017
- 26th September 2016
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Hongsheng Dai is a Lecturer in Statistics at the University of Essex, UK
Lecturer, University of Essex, UK.
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.
Statistician and Epidemiologist, Dundee Epidemiology and Biostatistics Unit, Population Health Sciences, University of Dundee, UK
"...an overview of recent developments in surviving analysis under truncation, especially for bivariate survival analysis...recommended to help statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias." --Zentralblatt MATH