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Multiple imputation by predictive mean matching in cluster-randomized trials.

Brittney E Bailey1, Rebecca Andridge2, Abigail B Shoben2

  • 1Department of Mathematics and Statistics, Amherst College, PO Box 5000, AC #2239, Amherst, 01002, MA, USA. bebailey@amherst.edu.

BMC Medical Research Methodology
|April 2, 2020
PubMed
Summary
This summary is machine-generated.

New predictive mean matching (PMM) methods improve multiple imputation (MI) for cluster randomized trials (CRTs). These PMM procedures offer more robust variance estimation and are less sensitive to model misspecification than existing techniques.

Keywords:
Cluster-randomized trialMissing dataMultiple imputationPredictive mean matching

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Random effects regression imputation is standard for multiple imputation (MI) in cluster randomized trials (CRTs), but is sensitive to model misspecification.
  • Existing MI software for multilevel data often ignores clustering or uses fixed effects, leading to biased variance estimates.
  • Predictive mean matching (PMM) offers a semiparametric alternative, but current implementations for multilevel data have limitations.

Purpose of the Study:

  • To develop novel MI procedures based on PMM that address the limitations of existing methods for CRTs.
  • To leverage opposing biases in variance estimates from PMM to create more accurate imputation models.
  • To evaluate the performance of new PMM-based MI methods against established techniques in CRTs.

Main Methods:

  • Developed three MI procedures using PMM: PMM-dist (weighting distance metric), PMM-avg (weighting imputed values average), and PMM-draw (weighted draw from imputed values).
  • Utilized Monte-Carlo simulations to assess the proposed methods, focusing on the estimation of treatment group means and variances.
  • Compared the performance of the new PMM methods against established MI procedures.

Main Results:

  • The proposed PMM procedures demonstrated reduced bias in MI variance estimation compared to established methods when the imputation model was correctly specified.
  • These PMM methods showed greater robustness to imputation model misspecification than traditional random effects imputation methods.
  • PMM-draw emerged as a particularly effective procedure for handling missing data in CRTs.

Conclusions:

  • The developed PMM-based MI procedures offer significant improvements for handling missing data in CRTs.
  • PMM-draw is highlighted as a promising and readily implementable method for multiply imputing missing data in CRTs.
  • These findings suggest that PMM-based imputation can enhance the accuracy and reliability of analyses in CRTs.