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Multiple imputation methods for missing multilevel ordinal outcomes.

Mei Dong1, Aya Mitani2

  • 1Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.

BMC Medical Research Methodology
|May 10, 2023
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) effectively handles missing multilevel ordinal outcomes. Fully conditional specification (FCS) with cluster size (CS) inclusion offers superior accuracy and power, especially with informative cluster sizes.

Keywords:
Generalized estimating equationsInformative cluster sizeMultilevel dataMultiple imputationOrdinal outcomes

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Multiple imputation (MI) is crucial for missing data in observational studies.
  • Joint modeling (JM) and fully conditional specification (FCS) are common for multilevel data.
  • MI for multilevel ordinal outcomes with informative cluster size (ICS) is understudied.

Purpose of the Study:

  • To compare MI strategies for multilevel ordinal outcomes with ICS.
  • Evaluate performance of CCA, FCS, FCS+CS, JM, and JM+CS.
  • Identify optimal imputation methods for specific scenarios.

Main Methods:

  • Monte Carlo simulation studies.
  • Comparison of five strategies: CCA, FCS, FCS+CS, JM, JM+CS.
  • Proportional odds logistic regression with cluster weighted generalized estimating equations (CWGEE).

Main Results:

  • Including cluster size (CS) in imputation improves accuracy with ICS.
  • FCS demonstrated more accurate and robust estimation than JM and CCA.
  • FCS+CS application in a dental study increased analysis power.

Conclusions:

  • MI enhances accuracy and power for missing ordinal outcomes.
  • FCS slightly outperforms JM for multilevel ordinal outcomes.
  • Recommend including CS in imputation when ICS is plausible.