Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects

Matching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which met...

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Veröffentlicht in:	American journal of epidemiology 2019-07, Vol.188 (7), p.1345-1354
Hauptverfasser:	Vable, Anusha M, Kiang, Mathew V, Glymour, M Maria, Rigdon, Joseph, Drabo, Emmanuel F, Basu, Sanjay
Format:	Artikel
Sprache:	eng
Schlagworte:	Causality Computer simulation Confidence intervals Confounding Factors, Epidemiologic Empirical analysis Epidemiologic Methods Error correction Exposure Humans Identification methods Least squares method Least-Squares Analysis Mathematical models Model matching Models, Statistical Practice of Epidemiology Propensity Score Regression analysis Statistical analysis
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