We describe a methodology for analysing transitions over time in a binary outcome variable that is subject to misclassification (that is, measurement error). Logistic regression models for transition events in the true underlying state are combined with estimates of probabilities of misclassification of the underlying state. 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. Hence 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 these restrictions are not satisfied then the conclusion from the analysis is that all observed transition events can be explained solely by the error in outcome assessment, in which case it is likely 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.
Copyright 2003 John Wiley & Sons, Ltd.