Use of a Markov transition model to analyse longitudinal low-back pain data

In a randomized clinical trial to assess the effectiveness of different strategies for treating low-back pain in a managed-care setting, 681 adult patients presenting with low-back pain were randomized to four treatment groups: medical care with and without physical therapy; and chiropractic care with and without physical modalities. Follow-up information was obtained by questionnaires at two and six weeks, six, 12 and 18 months and by a telephone interview at four weeks. One outcome measurement at each follow-up is the patient's self-report on the perception of low-back pain improvement from the previous survey, recorded as 'A lot better,' 'A little better,' 'About the same' and 'Worse.' Since the patient's perception of improvement may be influenced by past experience, the outcome is analysed using a transition (first-order Markov) model. Although one could collapse categories to the point that logistic regression analysis with repeated measurements could be used, here we allow for multiple categories by relating transition probabilities to covariates and previous outcomes through a polytomous logistic regression model with Markov structure. This approach allows us to assess not only the effects of treatment assignment and baseline characteristics but also the effects of past outcomes in analysing longitudinal categorical data.