Comparing Propensity Score Methods Versus Traditional Regression Analysis for the Evaluation of Observational Data: A Case Study Evaluating the Treatment of Gram-Negative Bloodstream Infections

• Published in: Clinical infectious diseases Vol. 71; no. 9; pp. e497 - e505
• Main Authors: Amoah, Joe, Stuart, Elizabeth A, Cosgrove, Sara E, Harris, Anthony D, Han, Jennifer H, Lautenbach, Ebbing, Tamma, Pranita D
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 03.12.2020
• Subjects: Cohort Studies; Humans; Logistic Models; Online Only; Propensity Score; Regression Analysis; Sepsis; propensity score matching; propensity score weighting; causal inference; observational data; logistic regression
• ISSN: 1058-4838; 1537-6591; 1537-6591
• DOI: 10.1093/cid/ciaa169

Abstract Background Propensity score methods are increasingly being used in the infectious diseases literature to estimate causal effects from observational data. However, there remains a general gap in understanding among clinicians on how to critically review observational studies that have incorporated these analytic techniques. Methods Using a cohort of 4967 unique patients with Enterobacterales bloodstream infections, we sought to answer the question “Does transitioning patients with gram-negative bloodstream infections from intravenous to oral therapy impact 30-day mortality?” We conducted separate analyses using traditional multivariable logistic regression, propensity score matching, propensity score inverse probability of treatment weighting, and propensity score stratification using this clinical question as a case study to guide the reader through (1) the pros and cons of each approach, (2) the general steps of each approach, and (3) the interpretation of the results of each approach. Results 2161 patients met eligibility criteria with 876 (41%) transitioned to oral therapy while 1285 (59%) remained on intravenous therapy. After repeating the analysis using the 4 aforementioned methods, we found that the odds ratios were broadly similar, ranging from 0.84–0.95. However, there were some relevant differences between the interpretations of the findings of each approach. Conclusions Propensity score analysis is overall a more favorable approach than traditional regression analysis when estimating causal effects using observational data. However, as with all analytic methods using observational data, residual confounding will remain; only variables that are measured can be accounted for. Moreover, propensity score analysis does not compensate for poor study design or questionable data accuracy. This article assists clinicians with understanding the relative pros and cons, the general steps involved, and the appropriate interpretation of the results of traditional regression analysis, propensity score matching, propensity score weighting, and propensity score stratification.