Survival Analysis in Medicine and Genetics
To Be Published June 18th 2013 by Chapman and Hall/CRC – 381 pages
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.
Introduction: Examples and Basic Principles
Design a Survival Study
Description of Survival Distribution
Analysis Trilogy: Estimation, Test, and Regression
Estimation of Survival Distribution
Analysis of Interval Censored Data
Definitions and Examples
Semiparametric Modeling with Case I Interval Censored Data
Semiparametric Modeling with Case II Interval Censored Data
Special Modeling Methodology
Multivariate Survival Data
Cure Rate Model
Diagnostic Medicine for Survival Analysis
Statistics in Diagnostic Medicine
Diagnostics for Survival Outcome under Diverse Censoring Patterns
Diagnostics for Right Censored Data
Survival Analysis with High-Dimensional Covariates
Identification of Marginal Association
Multivariate Prediction Models
Incorporating Hierarchical Structures
Exercises appear at the end of each chapter.
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.