Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study
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Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study. / Nguyen, Tri Long; Collins, Gary S.; Spence, Jessica; Devereaux, Philip J.; Daurès, Jean Pierre; Landais, Paul; Le Manach, Yannick.
I: Pharmacoepidemiology and Drug Safety, Bind 26, Nr. 12, 2017, s. 1513-1519.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study
AU - Nguyen, Tri Long
AU - Collins, Gary S.
AU - Spence, Jessica
AU - Devereaux, Philip J.
AU - Daurès, Jean Pierre
AU - Landais, Paul
AU - Le Manach, Yannick
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - adjustment
KW - causal inference
KW - confounding
KW - pharmacoepidemiology
KW - propensity score
U2 - 10.1002/pds.4325
DO - 10.1002/pds.4325
M3 - Journal article
C2 - 28984050
AN - SCOPUS:85037133759
VL - 26
SP - 1513
EP - 1519
JO - Pharmacoepidemiology and Drug Safety
JF - Pharmacoepidemiology and Drug Safety
SN - 1053-8569
IS - 12
ER -
ID: 218396490