Propensity score matching for causal inference and reducing the confounding effects: statistical standard and guideline of Life Cycle Committee

Since the development of research methodology, there has always been keen interest in developing the accuracy of the research by comparing covariates. Propensity score is useful when the research covers many variables which are not intended to be included as independent variables, thus allowing the removal of certain covariates from the model. This review discusses a general aspect of propensity score matching, which begins with the mathematical principles of propensity score matching. The concept and context of propensity score matching is also explained, which includes the advantages of propensity score matching over conventional research methods and the reasons for the introduction of propensity score matching in medical research. It is our aim that readers learn how to actually obtain a propensity score. Discussion of several options for performing matching based on the propensity score is also included, and the final topic is the adequacy of the matched cohort being evaluated with standardized mean differences, and research methods including Cox regression being conducted on the matched cohort. We hope to assist readers in understanding when and how to perform propensity score matching through this paper.