%0 Journal Article %1 Nguyen2017 %A Nguyen, Tri-Long %A Collins, Gary S %A Spence, Jessica %A Devereaux, Philip J %A Daurès, Jean-Pierre %A Landais, Paul %A Manach, Yannick Le %D 2017 %J Pharmacoepidemiology and drug safety %K adjustment causalinference confounding pharmacoepidemiology propensityscore %N 12 %P 1513-1519 %R 10.1002/pds.4325 %T Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study. %U http://doi.wiley.com/10.1002/pds.4325 http://www.ncbi.nlm.nih.gov/pubmed/28984050 %V 26 %X OBJECTIVE As covariates are not always adequately balanced after propensity score matching and double- adjustment can be used to remove residual confounding, we compared the performance of several double-robust estimators in different scenarios. METHODS We conducted a series of Monte Carlo simulations on virtual observational studies. After estimating the propensity scores by logistic regression, we performed 1:1 optimal, nearest-neighbor, and caliper matching. We used 4 estimators on each matched sample: (1) a crude estimator without double-adjustment, (2) double-adjustment for the propensity scores, (3) double-adjustment for the unweighted unbalanced covariates, and (4) double-adjustment for the unbalanced covariates, weighted by their strength of association with the outcome. RESULTS The crude estimator led to highest bias in all tested scenarios. Double-adjustment for the propensity scores effectively removed confounding only when the propensity score models were correctly specified. Double-adjustment for the unbalanced covariates was more robust to misspecification. Double-adjustment for the weighted unbalanced covariates outperformed the other approaches in every scenario and using any matching algorithm, as measured by the mean squared error. CONCLUSION Double-adjustment can be used to remove residual confounding after propensity score matching. The unbalanced covariates with the strongest confounding effects should be adjusted.

@article{Nguyen2017, abstract = {OBJECTIVE As covariates are not always adequately balanced after propensity score matching and double- adjustment can be used to remove residual confounding, we compared the performance of several double-robust estimators in different scenarios. METHODS We conducted a series of Monte Carlo simulations on virtual observational studies. After estimating the propensity scores by logistic regression, we performed 1:1 optimal, nearest-neighbor, and caliper matching. We used 4 estimators on each matched sample: (1) a crude estimator without double-adjustment, (2) double-adjustment for the propensity scores, (3) double-adjustment for the unweighted unbalanced covariates, and (4) double-adjustment for the unbalanced covariates, weighted by their strength of association with the outcome. RESULTS The crude estimator led to highest bias in all tested scenarios. Double-adjustment for the propensity scores effectively removed confounding only when the propensity score models were correctly specified. Double-adjustment for the unbalanced covariates was more robust to misspecification. Double-adjustment for the weighted unbalanced covariates outperformed the other approaches in every scenario and using any matching algorithm, as measured by the mean squared error. CONCLUSION Double-adjustment can be used to remove residual confounding after propensity score matching. The unbalanced covariates with the strongest confounding effects should be adjusted.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Nguyen, Tri-Long and Collins, Gary S and Spence, Jessica and Devereaux, Philip J and Daurès, Jean-Pierre and Landais, Paul and Manach, Yannick Le}, biburl = {https://www.bibsonomy.org/bibtex/258965393591cf7902e2dd281cc8f4c9b/jepcastel}, doi = {10.1002/pds.4325}, interhash = {786aa4607d04d55fdc9ba30322d17891}, intrahash = {58965393591cf7902e2dd281cc8f4c9b}, issn = {1099-1557}, journal = {Pharmacoepidemiology and drug safety}, keywords = {adjustment causalinference confounding pharmacoepidemiology propensityscore}, month = {12}, note = {Propensity score}, number = 12, pages = {1513-1519}, pmid = {28984050}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study.}, url = {http://doi.wiley.com/10.1002/pds.4325 http://www.ncbi.nlm.nih.gov/pubmed/28984050}, volume = 26, year = 2017 }