Analysis of longitudinal substance use outcomes using ordinal random-effects regression models

In this paper we describe analysis of longitudinal substance use outcomes using random-effects regression models (RRM). Some of the advantages of this approach is that these models allow for incomplete data across time, time-invariant and time-varying covariates, and can estimate individual change across time. Because substance use outcomes are often measured in terms of dichotomous or ordinal categories, our presentation focuses on categorical versions of RRM. Specifically, we present and describe an ordinal RRM that includes the possibility that covariate effects vary across the cutpoints of the ordinal outcome. This latter feature is particularly useful because a treatment can have varying effects on full versus partial abstinence, for example. Data from a smoking cessation study are used to illustrate application of this model for analysis of longitudinal substance use data.