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


METHODS
To 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.


RESULTS
The 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.


CONCLUSIONS
MSMs 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.
Suarez
David
D
Research & Development Unit, Sant Joan de Déu-SSM, Fundació Sant Joan de Déu, RETICS RD06/0011(REM-TAP Network), Sant Boi, Barcelona, Spain. david.suarez.lamas@gmail.com
Haro
Josep Maria
JM
Novick
Diego
D
Ochoa
Susana
S
D000328:Adult; D014150:Antipsychotic Agents / Q000627:therapeutic use; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012074:Remission Induction; D012559:Schizophrenia / Q000188:drug therapy; D016896:Treatment Outcome
D014150:Antipsychotic Agents