Modeling discrete time-to-event data.

*(English)*Zbl 1338.62006
Springer Series in Statistics. Cham: Springer (ISBN 978-3-319-28156-8/hbk; 978-3-319-28158-2/ebook). x, 259 p. (2016).

Publisher’s description: This 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.

##### MSC:

62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |

62N02 | Estimation in survival analysis and censored data |

62N03 | Testing in survival analysis and censored data |

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |

62P10 | Applications of statistics to biology and medical sciences; meta analysis |

62P05 | Applications of statistics to actuarial sciences and financial mathematics |

62G05 | Nonparametric estimation |