@article{ref1,
title="Bayesian criterion-based assessments of recurrent event models with applications to commercial truck driver behavior studies",
journal="Statistics in Medicine",
year="2022",
author="Zhang, Yiming and Chen, Ming-Hui and Guo, Feng",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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-event C-index quantifies adequacy of a given model in fitting the recurrent event data for each type, the between-event C-index provides an assessment of the model fit between two types of recurrent events, and the overall C-index measures the model's discrimination ability among multiple types of recurrent events simultaneously. Moreover, we jointly model the incidence of SCEs and on-duty breaks with driving behaviors using a Bayesian Poisson process model with time-varying coefficients and time-dependent covariates. An in-depth analysis of a real dataset from the commercial truck driver naturalistic driving study is carried out to demonstrate the usefulness and applicability of the proposed methodology.<p /> <p>Language: en</p>",
language="en",
issn="0277-6715",
doi="10.1002/sim.9528",
url="http://dx.doi.org/10.1002/sim.9528"
}