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Tuning multiple imputation by predictive mean matching and local residual draws
Tim P Morris1, Ian R White, Patrick Royston
1Hub for Trials Methodology Research, MRC Clinical Trials Unit at UCL, Aviation House, 125 Kingsway, WC2B 6NH, London, UK. tim.morris@ucl.ac.uk.
BMC Medical Research Methodology
|June 7, 2014
Summary
Predictive Mean Matching (PMM) and Local Residual Draws (LRD) offer robust alternatives to parametric imputation for missing data. However, careful model specification remains crucial for accurate results.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- Multiple imputation (MI) handles missing data but relies on correct parametric model specification.
- Predictive Mean Matching (PMM) and Local Residual Draws (LRD) offer more robust alternatives when models are misspecified.
Purpose of the Study:
- To review and clarify the use of PMM and LRD for handling incomplete covariates.
- To compare PMM and LRD performance against fully parametric imputation under various model specification scenarios.
Main Methods:
- Review of PMM and LRD methodologies and their variations.
- Simulation studies comparing PMM, LRD, and parametric imputation with correctly and incorrectly specified models.
Main Results:
- Advocates for using a pool of approximately 10 donors for PMM/LRD, not a single donor.
- Identifies optimal matching metrics and highlights limitations in current MI software implementations.
- PMM and LRD are suitable for covariates not strongly linked to outcomes and slight model misspecification.
Conclusions:
- PMM and LRD can be valuable for imputing covariates under specific conditions.
- Emphasizes that researchers should prioritize correct imputation model specification over relying solely on PMM or LRD.
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