Bayesian Analysis of Failure Time Data Using P-Splines
Matthias Kaeding, "Bayesian Analysis of Failure Time Data Using P-Splines"
English | ISBN: 3658083921 | 2015 | 120 pages | PDF | 3 MB
English | ISBN: 3658083921 | 2015 | 120 pages | PDF | 3 MB
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.