Tuning multiple imputation by predictive mean matching and local residual draws
Open accessAbstract
BACKGROUND: Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor's residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified. METHODS: We review development of PMM and LRD and outline the various forms available, and aim to clarify some choices about how and when they should be used. We compare performance to fully parametric imputation in simulation studies, first when the imputation model is correctly specified and then when it is misspecified. RESULTS: In using PMM or LRD we strongly caution against using a single donor, the default value in some implementations, and instead advocate sampling from a pool of around 10 donors. We also clarify which matching metric is best. Among the current MI software there are several poor implementations. CONCLUSIONS: PMM and LRD may have a role for imputing covariates (i) which are not strongly associated with outcome, and (ii) when the imputation model is thought to be slightly but not grossly misspecified. Researchers should spend efforts on specifying the imputation model correctly, rather than expecting predictive mean matching or local residual draws to do the work.
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All missing data for covariates were imputed using multiple imputation with predictive mean matching.
“All missing data for covariates were imputed using multiple imputation with predictive mean matching32.”
From Association of step counts over time with the risk of chronic disease in the All of Us Research Program (2022) · cites this paper as [32]
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- Authors
- Morris, T. P., White, I. R., & Royston, P.
- Affiliations
- MRC Clinical Trials Unit at UCL (GB)
- MRC Biostatistics Unit (GB)
- Corresponding author
- Morris, Tim P
- Venue
- BMC Medical Research Methodology
- Published
- 2014-06-05
- Volume
- 14
- Issue
- 1
- Pages
- 75-75
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC4051964
- OpenAlex
- W2058854776
- Cited by
- 568
- References
- 41
- Citations by year
- 2026: 45 · 2025: 64 · 2024: 81 · 2023: 68 · 2022: 70 · 2021: 63 · 2020: 56 · 2019: 44 · 2018: 32 · 2017: 23 · 2016: 12 · 2015: 6 · 2014: 2 · 2012: 1
- FWCI
- 15.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Medical Research Council (MC_EX_G0800814, U1052 60558)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- multiple imputation, predictive mean matching, missing at random, donor selection
- MeSH terms
- Carcinoma, Ovarian Epithelial, Albumins, Computer Simulation, Data Interpretation, Statistical, Humans, Neoplasms, Glandular and Epithelial, Ovarian Neoplasms, Serum Albumin, Models, Statistical, Biomedical Research
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