Marginal structural models for estimating effect modification.
OBJECTIVE
The use of marginal structural models (MSMs) to adjust for measured confounding factors is becoming increasingly common in observational studies. Here, we propose MSMs for estimating effect modification in observational cohort and case-control studies.


METHODS
MSMs for estimating effect modification were derived by the use of the potential outcome model. The proposed methods were applied to a cohort study and a case-control study.


RESULTS
In cohort studies, effect modification can be estimated by the application of a logistic MSM to individuals who experienced the event in question. In case-control studies, effect modification can be estimated by the ratio between the estimate from the model applied to case data and that applied to control data. The application of the model to real data from a cohort study indicated that the estimate from the proposed method was close to that from standard regression analysis. In a case-control study, the estimate from the proposed method may be biased.


CONCLUSIONS
Epidemiological researchers can use MSMs to estimate effect modification. In case-control studies, it should be determined whether the estimated effect modification is biased by applying a logistic MSM of control data.
Chiba
Yasutaka
Y
Department of Environmental Medicine and Behavioral Science, Kinki University School of Medicine, Osakasayama, Osaka, Japan. chibay@med.kindai.ac.jp
Azuma
Kenichi
K
Okumura
Jiro
J
D016022:Case-Control Studies; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D015987:Effect Modifier, Epidemiologic; D005260:Female; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D019370:Observation / Q000379:methods; D011247:Pregnancy