A Review of Propensity Score Application in Healthcare Outcome and Epidemiology

ABSTRACT Propensity score approaches in outcomes and epidemiological research are most often used for sample selection by matching, analysis of causal effect by stratification, or risk adjustment by combining propensity score and regression models. Several computing tools are available including SAS, S-PLUS/R, and SPSS to develop and implement propensity score approaches in a variety of applications. This paper reviews the history, statistical definitions, and application of the propensity score in non-randomized observational studies, pre-estimation for randomized designs, and poorly randomized experiments. Several distinct propensity score matching methods, both simple and sophisticated are described in detail to enable users to choose the most appropriate solutions to fit their study objectives. Special cases of propensity score applications discussed include multi-treatment studies, multi-control designs, and missing data processing, where the definitions, estimations and utilization of propensity scores are far different from the general, treatment-control approach. Finally, a discussion of the limitations of propensity score applications in health services and pharmaceutical outcomes research is included.