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

PSC In The News

RSS Feed icon

Buchmueller says employee wages are hit harder than corporate profits by rising health insurance costs

Davis-Kean et al. link children's self-perceptions to their math and reading achievement

Yang and Mahajan examine how hurricanes impact migration to the US

More News

Highlights

Pamela Smock elected to PAA Committee on Publications

Viewing the eclipse from ISR-Thompson

Paula Fomby to succeed Jennifer Barber as Associate Director of PSC

PSC community celebrates Violet Elder's retirement from PSC

More Highlights

Next Brown Bag

Mon, Oct 2, 2017, noon:
Valentina Duque, "Early-Life Shocks & Investment in Children's Education

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