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
"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
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.