Use of Propensity Scoring and Its Application to Real-World Data: Advantages, Disadvantages, and Methodological Objectives Explained to Researchers Without Using Mathematical Equations.

Real-time data collection of patient health status and medications is sped up with modern electronic devices and technologies. As real-world data provide enormous research opportunities, propensity score (PS) methods have been getting attention due to their theoretical grounds in a nonrandomized study setting. In contrast to randomized clinical trials, observational clinical data obtained from a real-world database may not have balanced distributions of patient characteristics between treatment and control groups at the beginning of the respective study. These imbalanced distributions may cause a bias in an estimated treatment effect, which needs to be eliminated. Propensity scoring is one class of statistical methods to address the imbalance issue of real-world data sets. This article provides basic concepts and assesses advantages, disadvantages, and methodological objectives of propensity scoring. Targeting clinical pharmacology researchers with limited statistical background, 5 representative methods are reviewed and visualized: matching, stratification, covariate modeling, inverse probability of treatment weighting, and doubly robust methods. Examples of applications of PS methods were selected from the literature of outcomes research and drug development, nephrology, and pediatrics. Opportunities of applications related to these examples are described. Furthermore, potential future applications of PS methods in clinical pharmacology are discussed. The 21st Century Cures Act signed in 2016 encourages scientists to find opportunities to apply propensity scoring to real-world data. This article underscores that scientists need to justify their choice of statistical methods, whether a PS method or an alternative method, based on their clinical study design, statistical assumptions, and research objectives.