Flexible bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effects

Thomas A. Murray, Brian P. Hobbs, Daniel J. Sargent, Bradley P. Carlin

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Presently, there are few options with available software to perform a fully Bayesian analysis of time-to-event data wherein the hazard is estimated semi- or non-parametrically. One option is the piecewise exponential model, which requires an often unrealistic assumption that the hazard is piecewise constant over time. The primary aim of this paper is to construct a tractable semiparametric alternative to the piecewise exponential model that assumes the hazard is continuous, and to provide modifiable, user-friendly software that allows the use of these methods in a variety of settings. To accomplish this aim, we use a novel model formulation for the log-hazard based on a low-rank thin plate linear spline that readily facilitates adjustment for covariates with time-dependent and proportional hazards effects, possibly subject to shape restrictions. We investigate the performance of our model choices via simulation. We then analyze colorectal cancer data from a clinical trial comparing the effectiveness of two novel treatment regimes relative to the standard of care for overall survival. We estimate a time-dependent hazard ratio for each novel regime relative to the standard of care while adjusting for the effect of aspartate transaminase, a biomarker of liver function, that is subject to a non-decreasing shape restriction.

Original languageEnglish (US)
Pages (from-to)381-402
Number of pages22
JournalBayesian Analysis
Volume11
Issue number2
DOIs
StatePublished - Jun 1 2016

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Keywords

  • Bayesian methods
  • Colorectal cancer
  • Penalized splines
  • Semiparametric methods
  • Shape-restricted effects
  • Survival analysis
  • Time-dependent effects

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability

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