Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Singh discusses her research in India on infertility

Johnston concerned declines in teen smoking threatened by e-cigarettes

Frey discusses book Diversity Explosion

Highlights

Apply for 2-year NICHD Postdoctoral Fellowships that begin September 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Next Brown Bag

Monday, Jan 12
Filiz Garip, Changing Dynamics of Mexico-U.S. Migration

Michael R. Elliott photo

The Effect of Duration and Delay of Licensure on Risk of Crash: a Bayesian Analysis of Repeated Time-to-Event Measures

Publication Abstract

Elliott, Michael R., Trivellore Raghunathan, and J.T. Shope. 2002. "The Effect of Duration and Delay of Licensure on Risk of Crash: a Bayesian Analysis of Repeated Time-to-Event Measures." Journal of the American Statistical Association, 97:420-431.

The driving history records of a sample of 13,794 Michigan public school students were followed for up to 13 years from their initial time-of-license to determine the separate effects of duration of licensure and delay of licensure on risk of crash. We propose a subject-specific lognormal accelerated failure time to model the expected time-to-crash as a function of age at time of licensure, duration of licensure, and a set of control covariates. When multiple time-to-crash measures are observed for an individual, within-subject correlation can create substantial bias in the estimation of the effect of duration of licensure under an independence model, Generalized estimating equations provide consistent estimators of the variance when independence is misspecified but do not correct for this bias, Full maximum Likelihood models generally require numerical integration and differentiation, and in practice, parameter estimates were unattainable for the dataset of interest. We instead adopt a Bayesian approach, imputing the unobserved failure times and slope-intercept random effects to account for right censoring and between-subject variability. We implement this approach using a Gibbs algorithm, We assess model fit via posterior predictive distributions, Our approach also allows for subject-specific risk estimates based on subject-level history. We compare the repeated sampling properties of this approach with those obtained using some frequentist approaches, and find that duration of licensure is a stronger predictor of risk of crash than age of licensure.

Browse | Search : All Pubs | Next