Bayesian Analysis of Failure Time Data Using P-Splines

✍ Scribed by Matthias Kaeding (auth.)

• Publisher: Springer Spektrum
• Year: 2015
• Tongue: English
• Leaves: 117
• Series: BestMasters
• Edition: 1
• Category: Library

✦ Synopsis

Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions. The MCMC methodology for these models is presented in a unified framework and applied on data sets. Among others, existing algorithms for the grouped Cox and the piecewise exponential model under interval censoring are combined with a data augmentation step for the applications. The author shows that the resulting Gibbs sampler works well for the grouped Cox and is merely adequate for the piecewise exponential model.

✦ Table of Contents

Front Matter....Pages I-IX
Introduction....Pages 1-4
Basic Concepts of Failure Time Analysis....Pages 5-16
Computation and Inference....Pages 17-44
Discrete Time Models....Pages 45-59
Application I: Unemployment Durations....Pages 61-68
Continuous Time Models....Pages 69-85
Application II: Crime Recidivism....Pages 87-94
Summary and Outlook....Pages 95-97
Back Matter....Pages 99-110

✦ Subjects

Probability Theory and Stochastic Processes; Laboratory Medicine; Bioinformatics