Tutz G., Schmid M. Modeling Discrete Time-to-Event Data

Springer International Publishing, Switzerland, 2016. – 252 p. – ISBN10: 3319281569This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.
Topics
Statistical Theory and Methods
Statistics for Life Sciences, Medicine, Health Sciences
Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
Statistics and Computing / Statistics ProgramsThe Life Table
Basic Regression Models
Evaluation and Model Choice
Nonparametric Modeling and Smooth Effects
Tree-Based Approaches
High-Dimensional Models: Structuring and Selection of Predictors
Competing Risks Models
Frailty Models and Heterogeneity
Multiple-Spell Analysis