Fitting multistate transition models with autoregressive logistic regression: supervised exercise in intermittent claudication.

Abstract

The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a six-month exercise program. The levels of the polytomous outcome variable correspond to states they defined in a Markov decision model comparing treatment strategies for intermittent claudication. Autoregressive logistic regression can be used to fit multistate transition models to observed longitudinal data with standard statistical software. The technique allows exploration of alternative assumptions about the dependence in the outcome series and provides transition probabilities for different covariate patterns. Of the alternatives examined, a Markov model including two preceding responses, time, age, ankle brachial index, and duration of disease best described the data.