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Handling missing data in a composite outcome with partially observed components: simulation study based on clustered

Susan Gachau1,2, Edmund Njeru Njagi3, Nelson Owuor2

  • 1Health Services Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.

Journal of Applied Statistics
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

Handling missing data in composite scores like the Paediatric Admission Quality of Care (PAQC) score is crucial. Multiple imputation (MI) of missing subcomponents offers less biased estimates than conventional zero-scoring methods for quality of care research.

Keywords:
Composite outcomePAQC scoremultiple imputationpaediatricspneumonia

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Pediatric Healthcare Quality

Background:

  • Composite scores are valuable for assessing complex quality of care processes.
  • Missing data in subcomponents can compromise the reliability of composite measures.
  • The Paediatric Admission Quality of Care (PAQC) score is an important ordinal composite outcome measure.

Purpose of the Study:

  • To evaluate strategies for handling missing data in the PAQC score.
  • To compare a conventional zero-imputation method with a multiple imputation (MI) approach.
  • To assess the impact of missing data on the reliability of composite quality of care scores.

Main Methods:

  • A simulation study was conducted to assess different missing data handling strategies.
  • The conventional method of scoring missing subcomponents as zero was compared to a latent normal joint modeling MI approach.
  • The study analyzed bias in parameter estimates and standard errors under various missingness scenarios.

Main Results:

  • Multiple imputation (MI) of missing PAQC score elements at the item level resulted in minimally biased estimates compared to the conventional zero-scoring method.
  • Regression coefficients were found to be more susceptible to bias than standard errors.
  • The extent of bias was influenced by the proportion of missing data and the underlying data generating mechanism.

Conclusions:

  • Careful handling of incomplete composite outcome subcomponents is essential to prevent biased estimates and misleading inferences in quality of care assessments.
  • The findings highlight the superiority of MI over conventional methods for missing PAQC score data.
  • Further research is recommended on alternative imputation strategies at component and composite outcome levels, compatible with the substantive model.