Marginal structural models might overcome confounding when analyzing multiple treatment effects in observational studies

ObjectiveWe review marginal structural models (MSMs) and show how they are useful when comparing the effects of multiple treatments on outcomes in observational studies. Until now, MSMs have not been used to compare the effects of more than two treatments.Study Design and SettingTo illustrate the application of MSMs when patients may receive several treatments, we have reanalyzed the effects of antipsychotic medication on achieving remission in schizophrenia using data from the SOHO study, a 3-year observational study of health outcomes associated with the treatment of schizophrenia.ResultsThe MSM results were, in general, consistent with but less statistically significant than those obtained using conventional methods. The MSM also showed qualitative differences in some comparisons in which the conventional analysis obtained results that were not consistent with previous knowledge.ConclusionMSMs can be used to analyze multiple treatment effects. MSMs, by using inverse-probability of treatment weights, might provide a better control for confounding than conventional methods by improving the adjustment for treatment group differences in observational studies, which may approximate their results to those of randomized controlled trials.