Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction

Methods for fitting survival regression models with a penalized smoothed hazard function have been recently discussed, even though they could be cumbersome. A simpler alternative which does not require specific software packages could be fitting a penalized piecewise exponential model. In this work the implementation of such strategy in WinBUGS is illustrated, and preliminary results are reported concerning the application of Bayesian P-splines techniques. The technique is applied to a pre-specified model in which the number and positions of knots were fixed on the basis of clinical knowledge, thus defining a non-standard smoothing problem.

[1]  Ludwig Fahrmeir,et al.  Propriety of posteriors in structured additive regression models: Theory and empirical evidence , 2009 .

[2]  Ciprian M. Crainiceanu,et al.  Bayesian Analysis for Penalized Spline Regression Using WinBUGS , 2005 .

[3]  Bradley P Carlin,et al.  Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effects. , 2016, Bayesian analysis.

[4]  Andreas Brezger,et al.  BayesX - Software for Bayesian Inference based on Markov Chain Monte Carlo simulation techniques , 2000 .

[5]  Murray Aitkin,et al.  A Reanalysis of the Stanford Heart Transplant Data , 1983 .

[6]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[7]  W. Gilks,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 1992 .

[8]  L. Fahrmeir,et al.  Bayesian inference for generalized additive mixed models based on Markov random field priors , 2001 .

[9]  Joseph G. Ibrahim,et al.  Bayesian Survival Analysis , 2004 .

[10]  S. Wood Generalized Additive Models: An Introduction with R , 2006 .

[11]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[12]  Göran Kauermann,et al.  Penalized spline smoothing in multivariable survival models with varying coefficients , 2005, Comput. Stat. Data Anal..

[13]  L. Mariani,et al.  Histology‐specific nomogram for primary retroperitoneal soft tissue sarcoma , 2010, Cancer.

[14]  David Ruppert,et al.  Semiparametric regression during 2003-2007. , 2009, Electronic journal of statistics.

[15]  Martyn Plummer,et al.  JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling , 2003 .

[16]  D. Gamerman Dynamic Bayesian Models for Survival Data , 1991 .

[17]  S. Lang,et al.  Bayesian P-Splines , 2004 .

[18]  R. Tibshirani,et al.  Varying‐Coefficient Models , 1993 .

[19]  Rob J Hyndman,et al.  Mixed Model-Based Hazard Estimation , 2002 .

[20]  Robert Gray,et al.  Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis , 1992 .

[21]  Andrew Gelman,et al.  R2WinBUGS: A Package for Running WinBUGS from R , 2005 .

[22]  J. Lawless Statistical Models and Methods for Lifetime Data , 2002 .

[23]  Paul H. C. Eilers,et al.  Flexible smoothing with B-splines and penalties , 1996 .