Best Practice Guidelines for Propensity Score Methods in Medical Research: Consideration on Theory, Implementation, and Reporting. A Review

Rigorous and reproducible methodology of controlling for bias is essential for high-quality, evidence-based studies. Propensity score matching (PSM) is a valuable way to control for bias and achieve pseudo-randomization in retrospective observation studies. The purpose of this review is to 1) provide a clear conceptual framework for PSM, 2) recommend how to best report its use in studies, and 3) offer some practical examples of implementation. First, this article covers the concepts behind PSM, discusses its pros and cons, and compares it with other methods of controlling for bias, namely, hard/exact matching and regression analysis. Second, recommendations are given for what to report in a manuscript when PSM is used. Finally, a worked example is provided, which can also serve as a template for the reader's own studies. A study's conclusions are only as strong as its methods. PSM is an invaluable tool for producing rigorous and reproducible results in observational studies. The goal of this article is to give practicing clinical physicians not only a better understanding of PSM and its implications but the ability to implement it for their own studies. Study Design: Review.

Automated summary

A rigorous and reproducible methodology for controlling bias is crucial in high-quality studies. Propensity score matching (PSM) is a valuable method for controlling bias and achieving pseudo-randomization in retrospective observation studies. This article aims to provide a clear conceptual framework for PSM, recommendations for reporting its use, and practical implementation examples. PSM is an invaluable tool for producing rigorous and reproducible results in observational studies.