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Multiple imputation for systematically missing effect modifiers in individual participant data meta-analysis.

Robert Thiesmeier1,2, Scott M Hofer2,3, Nicola Orsini1

  • 1Department of Global Public Health, Karolinska Institutet, Sweden.

Statistical Methods in Medical Research
|June 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a two-stage imputation method to handle missing data in individual participant data meta-analyses. The method provides unbiased estimates and improved precision for effect modifier analysis, even with limited trials.

Keywords:
Individual participant data meta-analysisMonte-Carlo simulationconditional quantile imputationsystematically missing datatreatment effect modificationtwo-stage meta-analysis

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Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Epidemiology

Background:

  • Individual participant data (IPD) meta-analysis is vital for identifying effect modifications.
  • Systematically missing data on effect modifiers (EMs) in IPD meta-analyses with few trials is under-explored.

Purpose of the Study:

  • To assess the impact of systematically missing data on discrete EMs in IPD meta-analyses.
  • To evaluate a two-stage imputation method for handling such missing data.

Main Methods:

  • Simulated IPD meta-analyses with systematically missing EM data across multiple studies.
  • Employed a multivariable Weibull survival model to assess treatment effects across EM levels (beneficial, null, harmful).
  • Utilized Monte-Carlo simulations to evaluate bias and coverage, comparing common and heterogeneous effect models.

Main Results:

  • The two-stage imputation method yielded low absolute bias (<0.016 for common effects, <0.007 for heterogeneous effects).
  • Coverage remained close to nominal values across all EM levels.
  • Uncongenial imputation models increased bias, even with minimal missing data.

Conclusions:

  • The proposed two-stage imputation approach effectively handles systematically missing data in IPD meta-analyses.
  • This method offers unbiased estimates and enhanced precision for analyzing effect modifiers.
  • Careful consideration of imputation model assumptions is crucial for accurate results.