Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review

Abstract

Objectives: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysis were extracted, such as covariates, matching algorithm, and reporting of estimated treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%) case-control studies were found among 171 identified articles. Forty-five studies (26.32%) provided a reasonable justification for covariates selection. One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide the model that was used for calculating the propensity score or did not report the matching algorithm, respectively. Seventy-three (42.69%) studies reported the method(s) for checking covariates balance. Forty studies (23.39%) had a statistician co-author. All the case-control studies (n=13) did not have a statistician co-author (p=0.006) and all studies that clarified the treatment effect estimation (n=6) had a statistician co-author (p<0.001). Conclusions: The reporting quality of the PSM analysis is suboptimal in some COVID-19 epidemiological studies. Some pitfalls may undermine study findings that involve PSM analysis, such as a mismatch between PSM analysis and study design.