Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data
Open accessAbstract
(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score.
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Missing data were handled using methods that can provide unbiased parameter estimates under a missing at random assumption.
“We, therefore, where possible implemented methods that can provide unbiased parameter estimates under a ‘missing at random’ (MAR) assumption (see Bottigliengo et al., 2021).”
From A counterfactual and random intercept cross‐lagged panel analysis of the effects of reading frequency on adolescent mental health in a large longitudinal study (2026) · cites this paper as Bottigliengo et al., 2021
The Discussion (page not provided) says the methods considered rely on MCAR or MAR assumptions and recommends methods that account for missing data when MAR is plausible. It also says the study assessed covariate balance only; whether the methods yield unbiased estimates was left for future research. Thus the paper supports the MAR-based approach, but not the claim about unbiased parameter estimates. (LLM, conf=0.85)
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- Authors
- Bottigliengo, D., Lorenzoni, G., Ocagli, H., Martinato, M., Berchialla, P., & Gregori, D.
- Affiliations
- University of Padua (IT)
- University of Turin (IT)
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy
- ORCID
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2021-06-22
- Volume
- 18
- Issue
- 13
- Pages
- 6694
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC8293809
- OpenAlex
- W3174942049
- Cited by
- 13
- References
- 73
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Brown University
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- propensity score, missing data, baseline covariates, multiple imputation, expectation-maximization algorithm, complete case analysis, covariate balance, missing at random, left main coronary artery disease, observational studies, sensitivity analysis
- MeSH terms
- Data Interpretation, Statistical, Humans, Longitudinal Studies, Research Design, Models, Statistical, Propensity Score
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