The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance
Open accessAbstract
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.
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“residual imbalance was handled through double adjustment in the outcome analyses to achieve double robustness to potential model misspecification.46,47”
From Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study (2025) · cites this paper as [47]
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The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
A simulation study of the number of events per variable in logistic regression analysis
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00236-3 · Cited by 9,655
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Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
Vittinghoff, E., & McCulloch, C. E. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk052 · Cited by 4,359
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Lee, T. H.-Y., Marcantonio, E. R., Mangione, C. M., Thomas, E. J., Polanczyk, C. A., Cook, E. F., Sugarbaker, D. J., Donaldson, M. C., Poss, R., Ho, K. K. L., et al. (1999) · Circulation · doi:10.1161/01.cir.100.10.1043 · Cited by 3,749
Variable Selection for Propensity Score Models
Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj149 · Cited by 2,336
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Research works printed but not linked: 1
- Ho DE, Imai K, King G, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2006;15(3):199–236.
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Metadata
- Authors
- Nguyen, T.-L., Collins, G. S., Spence, J., Daurès, J.-P., Devereaux, P. J., Landais, P., & Le Manach, Y.
- Affiliations
- Université de Montpellier (FR)
- Population Health Research Institute (CA)
- Venue
- BMC Medical Research Methodology
- Published
- 2017-04-28
- Volume
- 17
- Issue
- 1
- Pages
- 78-78
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC5408373
- OpenAlex
- W2609185034
- Cited by
- 512
- References
- 35
- Citations by year
- 2026: 65 · 2025: 79 · 2024: 76 · 2023: 76 · 2022: 86 · 2021: 61 · 2020: 32 · 2019: 19 · 2018: 12 · 2017: 5
- FWCI
- 12.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, other-oa)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- None
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- propensity score matching, standardized mean difference, covariate balance, regression adjustment, confounding, nearest neighbor matching, treatment effect estimation, mean squared error
- MeSH terms
- Algorithms, Computer Simulation, Humans, Monte Carlo Method, Models, Statistical, Bias, Logistic Models, Propensity Score
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