Bayesian criterion-based assessments of recurrent event models with applications to commercial truck driver behavior studies.

ABSTRACT: Multitype recurrent events are commonly observed in transportation studies, since commercial truck drivers may encounter different types of safety critical events (SCEs) and take different lengths of on-duty breaks in a driving shift. Bayesian nonhomogeneous Poisson process models are a flexible approach to jointly model the intensity functions of the multitype recurrent events. For evaluating and comparing these models, the deviance information criterion (DIC) and the logarithm of the pseudo-marginal likelihood (LPML) are studied and Monte Carlo methods are developed for computing these model assessment measures. We also propose a set of new concordance indices (C-indices) to evaluate various discrimination abilities of a Bayesian multitype recurrent event model. Specifically, the within-

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9796651 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature