Bayesian regression model for recurrent event data with event-varying covariate effects and event effect

ABSTRACT

In the course of hypertension, cardiovascular disease events (e.g. stroke, heart failure) occur frequently and recurrently. The scientific interest in such study may lie in the estimation of treatment effect while accounting for the correlation among event times. The correlation among recurrent event times comes from two sources: subject-specific heterogeneity (e.g. varied lifestyles, genetic variations, and other unmeasurable effects) and event dependence (i.e. event incidences may change the risk of future recurrent events). Moreover, event incidences may change the disease progression so that there may exist event-varying covariate effects (the covariate effects may change after each event) and event effect (the effect of prior events on the future events). In this article, we propose a Bayesian regression model that not only accommodates correlation among recurrent events from both sources, but also explicitly characterizes the event-varying covariate effects and event effect. This model is especially useful in quantifying how the incidences of events change the effects of covariates and risk of future events. We compare the proposed model with several commonly used recurrent event models and apply our model to the motivating lipid-lowering trial (LLT) component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) (ALLHAT-LLT).

KEYWORDS:

• Event dependence
• frailty model
• heterogeneity
• Markov chain Monte Carlo
• survival model

Acknowledgments

The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing high-performing computing resources that have contributed to the research results reported within this article. URL: http://www.tacc.utexas.edu.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Li-An Lin  http://orcid.org/0000-0003-2731-1346

Sheng Luo  http://orcid.org/0000-0003-4214-5809

Additional information

Funding

Sheng Luo's research was supported in part by the National Institute of Neurological Disorders and Stroke under Award Numbers R01NS091307 and 5U01NS043127. Barry Davis's research was supported in part by Health and Human Services contracts N01-HC-35130 and HHSN268201100036C from the National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD.