Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance

Research output: Contribution to journalJournal articleResearchpeer-review


  • Nguyen, Long
  • Gary S. Collins
  • Jessica Spence
  • Jean Pierre Daurès
  • P. J. Devereaux
  • Paul Landais
  • Yannick Le Manach

Background: Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods: We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results: We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion: If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering.

Original languageEnglish
Article number78
JournalBMC Medical Research Methodology
Issue number1
Number of pages8
Publication statusPublished - 2017
Externally publishedYes

    Research areas

  • Causal inference, Confounding, Covariate balance, Propensity score

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