Transitions in an imperfectly observed binary variable: depressive symptomatology in adolescents
by
Rory Wolfe
Monash University
Coauthors: John B Carlin (Murdoch Children's Research Institute and University of Melbourne.), George C Patton (Centre for Adolescent Health, University of Melbourne)

We describe a methodology for analyzing transitions over time in a binary outcome variable that is subject to misclassification (i.e. measurement error). Logistic regression models for transition events in the true underlying state are combined with estimates of misclassification probabilities. The model is based on the Markovian assumption that the probabilities of transition in the underlying state at a given time depend only on the underlying state at the previous time.

Framing the problem in a Bayesian form and using Monte Carlo Markov Chain methods we estimate odds-ratio effects for transitions that are adjusted for the effect of misclassification. Comparing these adjusted estimates with estimates that are obtained without taking misclassification into account indicates that the latter can be biased either toward or away from the null.

For the estimates to exist, certain restrictions on the observed data and misclassification probabilities need to be met. If the restrictions are not satisfied then the model effectively says that all observed transition events can be explained solely by the error in outcome assessment in which case it may be that an aspect of the model is incorrect. The motivation for this work comes from an analysis of transitions in depression status for a cohort of Australian teenagers participating in a longitudinal study of adolescent health.

Date received: April 4, 2002