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

• Published in: Journal of applied statistics Vol. 45; no. 7; pp. 1260 - 1276
• Main Authors: Lin, Li-An, Luo, Sheng, Davis, Barry R.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis Ltd 01.01.2018
• Subjects: Bayesian analysis; Correlation; Diuretics; Heart; Hypertension; Regression models; Statistical methods; Event dependence; Frailty model; Heterogeneity; Markov Chain Monte Carlo; Survival model
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2017.1367368

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 come 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).