Adjusting Complex Heterogeneity in Treatment Assignment in Observational Studies

Treatment assignment in observational studies is complex and can be influenced by many factors that include patient characteristics, physician practices, and health care systems. These influences can present heterogeneity or clustering effects in the treatment assignment. If those heterogeneity or clustering effects are not appropriately adjusted, the estimated treatment effect may be severely biased. Through a series of models that mimic various level of heterogeneity in treatment assignment in observational studies, we evaluate, through simulation study, the performance of several estimators under the impact of different types of heterogeneity. These estimators include propensity score stratification, propensity score inverse probability weighting, propensity score regression and the partial least squares method. Our results suggest that the partial least squares method is most robust while the dummy variable adjustment method in propensity regression also performs fairly consistently. We use the proposed method to analyze a data set from the German Breast Cancer Study Group study.