Title: Doubly robust matching estimators for high dimensional confounding adjustment.
Authors: Antonelli, Joseph; Cefalu, Matthew; Palmer, Nathan; Agniel, Denis
Published In Biometrics, (2018 12)
Abstract: Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.
PubMed ID: 29750844
MeSH Terms: Bias; Computer Simulation; Confounding Factors, Epidemiologic*; Female; Humans; Insurance Claim Review; Male; Observational Studies as Topic/standards; Opioid-Related Disorders/epidemiology; Outcome Assessment, Health Care/methods*; Outcome Assessment, Health Care/standards; Prognosis; Propensity Score; Sex Factors; Statistics as Topic/methods*; Substance-Related Disorders/epidemiology