Bayesian Analysis of Failure Time Data Using P-Splines
Bayesian Analysis of Failure Time Data Using P-Splines
Springer | Anatomy, Bioinformatics, Molecular Biology | Dec 27 2014 | ISBN-10: 3658083921 | 110 pages | pdf | 2.95 mb
Springer | Anatomy, Bioinformatics, Molecular Biology | Dec 27 2014 | ISBN-10: 3658083921 | 110 pages | pdf | 2.95 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.
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