Survival Analysis in Medicine and Genetics (Hardback) book cover

Survival Analysis in Medicine and Genetics

By Jialiang Li, Shuangge Ma

© 2013 – Chapman and Hall/CRC

381 pages | 36 B/W Illus.

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About the Book

Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields.

The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples.

Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.


". . . this book contains an excellent theoretical coverage of interval censored data, and deals with other topics relevant for survival analysis in a comprehensive but summarized way."

—Victor Moreno, International Society for Clinical Biostatistics

"This book provides a new outlook on survival analysis methods by emphasizing the application of the statistical methods for biological and genetic problems. … this book covers several important and specific topics, which have been rarely covered in other conventional survival textbooks. Throughout this book, many advanced statistical methods are well specified so that biostatisticians and researchers in the fields of medicine and genetics can easily understand and apply these methods to complicated survival data with high-dimensional covariates."

—Seungyeoun Lee, Biometrics, March 2014

Table of Contents

Introduction: Examples and Basic Principles


Design a Survival Study

Description of Survival Distribution

Censoring Mechanisms

Analysis Trilogy: Estimation, Test, and Regression

Estimation of Survival Distribution

Two-Sample Comparison

Regression Analysis


Theoretic Notes

Analysis of Interval Censored Data

Definitions and Examples

Parametric Modeling

Nonparametric Modeling

Two-Sample Comparison

Semiparametric Modeling with Case I Interval Censored Data

Semiparametric Modeling with Case II Interval Censored Data



Special Modeling Methodology

Nonparametric Regression

Multivariate Survival Data

Cure Rate Model

Bayesian Analysis

Theoretic Notes

Diagnostic Medicine for Survival Analysis

Statistics in Diagnostic Medicine

Diagnostics for Survival Outcome under Diverse Censoring Patterns

Diagnostics for Right Censored Data

Theoretic Notes

Survival Analysis with High-Dimensional Covariates


Identification of Marginal Association

Multivariate Prediction Models

Incorporating Hierarchical Structures

Integrative Analysis





Exercises appear at the end of each chapter.

About the Authors

Jialiang Li is an associate professor in the Department of Statistics and Applied Probability at the National University of Singapore, an associate professor at the Duke-NUS Graduate Medical School, and a scientist at the Singapore Eye Research Institute. He is on the editorial board of Biometrics and has published 70 peer-reviewed research papers in scientific journals. He has been a recipient the Young Scientist Award from the National University of Singapore and the New Investigator Grant and Cooperative Basic Research Grant from the National Medical Research Council.

Shuangge Ma is an associate professor in the Department of Biostatistics, Yale School of Public Health at Yale University. He earned a PhD in statistics from the University of Wisconsin and completed postdoctoral training in the Department of Biostatistics at the University of Washington. His research interests include survival analysis, semiparametric methods, bioinformatics, cancer studies, and health economics.

About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General