Assessing risk-taking in a driving simulator study: Modeling longitudinal semi-continuous driving data using a two-part regression model with correlated random effects

Publication date: January 2015 Source:Analytic Methods in Accident Research, Volumes 5–6 Author(s): Van Tran , Danping Liu , Anuj K. Pradhan , Kaigang Li , C. Raymond Bingham , Bruce G. Simons-Morton , Paul S. Albert Signalized intersection management is a common measure of risky driving in simulator studies. In a recent randomized trial, investigators were interested in whether teenage males exposed to a risk-accepting passenger took more intersection risks in a driving simulator compared with those exposed to a risk-averse peer passenger. Analyses in this trial are complicated by the longitudinal or repeated measures that are semi-continuous with clumping at zero. Specifically, the dependent variable in a randomized trial looking at the effect of risk-accepting versus risk-averse peer passengers on teenage simulator driving is comprised of two components. The discrete component measures whether the teen driver stops for a yellow light, and the continuous component measures the time the teen driver, who does not stop, spends in the intersection during a red light. To convey both components of this measure, we apply a two-part regression with correlated random effects model (CREM), consisting of a logistic regression to model whether the driver stops for a yellow light and a linear regression to model the time spent in the intersection during a red light. These two components are related through the correlation of their random effects. Using this novel analysis, ...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research