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Combining multiple imputation and meta-analysis with individual participant data.

Stephen Burgess1, Ian R White, Matthieu Resche-Rigon

  • 1Department of Public Health & Primary Care, Strangeways Research Laboratory, 2 Worts Causeway, Cambridge, CB1 8RN, U.K.

Statistics in Medicine
|May 25, 2013
PubMed
Summary

Multiple imputation methods can mitigate missing data issues in pooled analyses. Ensuring imputation and analysis models are congenial is crucial for accurate estimates and confidence intervals in meta-analysis.

Keywords:
Rubin's rulesindividual participant datameta-analysismissing datamultiple imputation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research

Background:

  • Incomplete data is common in multi-study analyses, impacting statistical power and estimate bias.
  • Choosing between within-study and multilevel imputation models for covariates is unclear.
  • Combining imputation techniques with inverse-variance weighted meta-analysis requires careful consideration.

Purpose of the Study:

  • To investigate the impact of imputation model choice on handling missing covariate data in meta-analysis.
  • To provide guidance on combining multiple imputation with inverse-variance weighted meta-analysis.
  • To ensure the congeniality of imputation and analysis models for accurate statistical inference.

Main Methods:

  • Simulation analysis of sporadically missing data in a single covariate with a linear model.
  • Discussion of applicability to systematically missing data across studies.
  • Application of Rubin's rules at the study level prior to meta-analysis.

Main Results:

  • Congeniality between imputation and analysis models is essential for correct standard errors and confidence intervals.
  • Incorporating between-study heterogeneity into the imputation model maintains model congeniality.
  • Imputing data at the study level before meta-analysis is recommended over meta-analyzing each imputation separately.

Conclusions:

  • The choice and implementation of multiple imputation models significantly affect meta-analysis results.
  • Maintaining model congeniality is key for reliable estimation in the presence of missing data.
  • Study-level imputation followed by meta-analysis is the preferred approach for pooled analyses with missing data.