Performance of Matching Methods to Unmatched Ordinary Least Squares Regression Under Constant Effects.

Performance of Matching Methods to Unmatched Ordinary Least Squares Regression Under Constant Effects. Am J Epidemiol. 2019 Apr 17;: Authors: Vable AM, Kiang MV, Glymour MM, Rigdon J, Drabo EF, Basu S Abstract Matching methods are assumed to reduce the likelihood of a biased inference compared to ordinary least squares regression. Using simulations, we compare inferences from propensity score matching, coarsened exact matching, and un-matched covariate-adjusted ordinary least squares regression (OLS) to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple datasets and systematically varied common support, discontinuities in the exposure and / or outcome, exposure prevalence, and analytic model misspecification. Matching inferences were often biased compared to OLS, particularly when common support was poor; when analysis models were correctly specified and common support was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 18.2% for coarsened exact matching (high), and 4.8% for OLS (expected). Our results suggest when estimates from matching and OLS are similar (i.e. confidence intervals overlap), OLS inferences are unbiased more often than matching inferences, however, when estimates from matching and OLS are dissimilar (i.e. confidence intervals do not overlap), matching inferences are unbiased more ofte...
Source: Am J Epidemiol - Category: Epidemiology Authors: Tags: Am J Epidemiol Source Type: research