Propensity-Score Matching: Optimal, Adequate, or Incomplete?

The gold standard for comparing two or more therapies in modern medicine is the prospective, double-blind, randomized clinical trial. In this model, especially with sizable enrollment, randomization is assumed to equalize outcome-influencing factors across study arms such that they have no unbalanced effect on outcomes tested. Recently, clinical studies have increasingly utilized registries, electronic medical records, and other real-world observational data sets in place of or to supplement randomized trials. Notably, nonrandomized studies are subject to confounding when enrollees who receive one treatment under investigation differ systematically from those receiving another, including selection bias where patient treatments are chosen by their physicians rather than by randomization. In such studies, statistical adjustment by propensity-score matching (PSM) is commonly employed in an attempt to reduce bias from concomitant confounding variables (to correct for many baseline imbalances). PSM attempts to mimic randomization on observed covariates. PSM is also frequently used in post-hoc, retrospective, and subgroup analyses for similar reasons. Importantly, while PSM is valuable, it is not all-inclusive. It is not readily apparent that practitioners recognize this clinically important limitation and consider it when interpreting/applying study results. This paper provides supporting details regarding the above, and uses a comparison of two atrial fibrillation trials and two CHA2DS2-VASc circumstances to enhance the points made.