Survival Analysis in Medicine and Genetics  book cover
1st Edition

Survival Analysis in Medicine and Genetics

ISBN 9781439893111
Published June 4, 2013 by Chapman and Hall/CRC
381 Pages 36 B/W Illustrations

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Book Description

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.

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.

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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.


". . . 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

"The great strength of the book lies in its comprehensive treatment of both classical and novel methods, covering almost all aspects of survival analysis that biostatisticians are confronted with in everyday practice. The text is very well organised, and both writing style and notation are remarkably homogeneous. The readers will appreciate the inclusion of clinical studies as applications in the book."
—P. G. Sankaran, Cochin University of Science and Technology