Ordinal Latent Variable Models and Their Application to Newly Licensed Teenage Drivers

• Activity Number: 20
• Type: Topic Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): John Jackson*+
• Companies: U.S. Military Academy
• Keywords: ordinal latent model ; driving study ; hidden Markov

Abstract

Understanding the association between risky driving and crash events helps yield insight to teenage driving behavior. Further, prediction of crash events from previously observed kinematic behavior is important from a public health perspective. The Naturalistic Teenage Driving Study (NTDS) is the first U.S. study to document continuous driving performance and crash/near crash experience of newly-licensed teenagers during their first 18 months of licensure. Here we use counts of certain kinematic events to describe risky driving behavior. We present binary and ordinal latent variable models for investigating these associations. A random effects model introduces heterogeneity among subjects in modeling the mean value of the latent state; we discuss the estimation of these models using the EM algorithm for the models not including random effects, and the Monte Carlo EM algorithm for the random effects models. Time permitting we will discuss joint models where the crash outcomes are linked to the kinematic measures with a hidden Markov model for the latent states.