Propensity score-based methods for causal inference and external data leveraging in regulatory settings: From basic ideas to implementation.

The breakthrough propensity score methodology was formulated by Rosenbaum and Rubin in the 1980s for the mitigation of confounding bias in non-randomized comparative studies to facilitate causal inference for treatment effects. The methodology had been used mainly in epidemiological and social science studies that may often be exploratory, until it was adopted by FDA/CDRH in 2002 and applied in the evaluation of medical device pre-market confirmatory studies, including those with a control group extracted from a well-designed and executed registry database or historical clinical studies. Around 2013, following the Rubin outcome-free study design principle, the two-stage propensity score design framework was developed for medical device studies to safeguard study integrity and objectivity, thereby strengthening the interpretability of study results. Since 2018, the scope of the propensity score methodology has been broadened so that it can be used for the purpose of leveraging external data to augment a single-arm or randomized traditional clinical study. All these statistical approaches, collectively referred to as propensity score-based methods in this article, have been considered in the design of medical device regulatory studies and stimulated related research, as evidenced by the latest trends in journal publications. We will provide a tutorial on the propensity score-based methods from the basic idea to their implementation in regulatory settings for causal inference and external data leveraging, along with step-by-step descriptions of the procedures of the two-stage outcome-free design through examples, which can be used as templates for real study proposals.