Use of Propensity Score Methodology in Contemporary High-Impact Surgical Literature

Background: Propensity score (PS) analysis is a statistical method commonly used in observational trials to account for confounding. Improper use of PS analysis can bias the effect estimate. The aim of this study is to review the use and reporting of PS methods in high-impact surgical journals with a focus on propensity score matching (PSM).

Study design: The 10 surgical journals with the highest impact factors were searched to identify studies using PS analysis from January 1, 2016 to December 14, 2018. We selected evaluation criteria for the conduct of PS analysis based on previous reports. Two authors systematically appraised the quality of reporting of PS analyses. Univariate and multivariate regression was performed to determine the relationship between appropriate use of PSM and study conclusion.

Results: Three hundred and three studies using PS analysis were included. Ninety-one percent (n = 275) of studies included the covariates used to generate the PS and 79% (n = 239) included the type of regression model used. Ninety percent (n = 272) of studies did not justify the covariates included in their PS. Eighty-four percent of studies used PSM (n = 254), with 48% (n = 123) failing to assess covariate balance between groups. We found that justification of the selection of covariates included in the PS and the characterization of unmatched patients were both associated with lower odds of the study finding a significant result (odds ratio 0.37; 95% CI 0.16 to 0.87; p = 0.02 and odds ratio 0.35; 95% CI 0.17 to 0.75; p = 0.007, respectively, at multivariate logistic regression).

Conclusions: This study demonstrates that even in research published in high-quality surgical journals, several studies report their PS methodology inadequately. The inadequate conduct of PS analysis can impact a study's conclusion.