A mixed-effects quintile-stratified propensity adjustment for effectiveness analyses of ordered categorical doses

Observational studies can be used to evaluate treatment effectiveness among patients with a broader range of illness severity than typically seen in randomized controlled clinical trials. However, there are several difficulties with observational evaluations including non-equivalent comparison groups, treatment doses and durations that vary widely, and, in longitudinal studies, multiple courses of treatment per subject. A mixed-effects approach to the propensity adjustment for non-equivalent comparison groups is described that can account for each of these perturbations. The strategy involves two stages. First, characteristics that distinguish among subjects who receive various levels of treatment are examined in a model of propensity for treatment intensity using mixed-effects ordinal logistic regression. Second, the propensity-stratified effectiveness of ordered categorical doses is compared in a mixed-effects grouped time survival model of time until recovery. The model is applied in a longitudinal, observational study of antidepressant effectiveness. Then a Monte Carlo simulation study indicates that the strategy has acceptable type I error rates and minimal bias in the estimates of treatment effectiveness. Statistical power exceeds 0.90 for an odds ratio of 1.5 with N = 250 and 500, and is acceptable for an odds ratio of 2.0 with N = 100. Nevertheless, with N = 100, the models that had high intraclass correlation coefficients had greater tendency towards non-convergence. This approach is a useful strategy for observational studies of treatment effectiveness. It is capable of adjusting for selection bias, incorporating multiple observations per subject, and comparing effectiveness of ordinal doses.