Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions

Stat Methods Med Res. 2017 Dec;26(6):2869-2884. doi: 10.1177/0962280215613378. Epub 2015 Nov 6.

Abstract

Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial.

Keywords: Markov Chain Monte Carlo; Recurrent events; hypertension; joint model; multivariate frailty model.

MeSH terms

  • Anticholesteremic Agents / therapeutic use
  • Bayes Theorem*
  • Biostatistics / methods
  • Cardiovascular Diseases / mortality
  • Cardiovascular Diseases / prevention & control
  • Computer Simulation
  • Humans
  • Hypertension / drug therapy
  • Likelihood Functions
  • Longitudinal Studies
  • Markov Chains
  • Models, Statistical*
  • Monte Carlo Method
  • Multivariate Analysis
  • Proportional Hazards Models
  • Randomized Controlled Trials as Topic / statistics & numerical data
  • Recurrence
  • Statistics, Nonparametric*

Substances

  • Anticholesteremic Agents