Open AccessComparing Propensity Score Methods Versus Traditional Regression Analysis for the Evaluation of Observational Data: A Case Study Evaluating the Treatment of Gram-Negative Bloodstream Infections
Author(s) -
Joe Amoah,
Elizabeth A. Stuart,
Sara E. Cosgrove,
Anthony D. Harris,
Jennifer H. Han,
Ebbing Lautenbach,
Pranita D. Tamma
Publication year - 2020
Publication title -
clinical infectious diseases/clinical infectious diseases (online. university of chicago. press)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.44
H-Index - 336
eISSN - 1537-6591
pISSN - 1058-4838
DOI - 10.1093/cid/ciaa169
Subject(s) - propensity score matching , observational study , medicine , confounding , logistic regression , marginal structural model , inverse probability weighting , causal inference , intensive care medicine , statistics , pathology , mathematics
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.