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.
| Original language | English |
|---|---|
| Journal | Pharmacoepidemiology and Drug Safety |
| Volume | 26 |
| Issue number | 12 |
| Pages (from-to) | 1513-1519 |
| Number of pages | 7 |
| ISSN | 1053-8569 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
Keywords
- adjustment
- causal inference
- confounding
- pharmacoepidemiology
- propensity score