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Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data
Matthieu Resche-Rigon1, Ian R White, Jonathan W Bartlett
1MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.; DBIM, Hôpital Saint-Louis, APHP, Paris, France; Université Paris Diderot, Paris, France; Inserm UMRS 717, Paris, France.
Statistics in Medicine
|July 17, 2013
Summary
This study introduces multiple imputation to address systematically missing confounders in meta-analyses. This method effectively adjusts for missing data, reducing bias in observational epidemiology research.
Area of Science:
- Epidemiology
- Biostatistics
- Medical Research
Background:
- Systematically missing variables, absent in entire studies, pose challenges in meta-analyses.
- Standard methods fail to adjust for these missing confounders or exclude relevant studies.
Purpose of the Study:
- To propose and evaluate a novel approach for adjusting exposure-disease associations for systematically missing confounders.
- To compare the proposed method with existing techniques in meta-analysis.
Main Methods:
- Multiple imputation by chained equations using multilevel regression models to impute systematically missing data.
- Accounting for between-study heterogeneity during data imputation.
- Simulation studies and a real-world data illustration using carotid intima-media thickness and cardiovascular events.
Main Results:
- Multiple imputation effectively handles systematic missingness patterns.
- The proposed method reduces bias compared to standard approaches.
- Including between-study random effects in imputation models further minimizes bias.
Conclusions:
- Multiple imputation is a practical and effective strategy for addressing systematically missing confounders in individual participant data meta-analyses.
- The approach enhances the reliability of observational epidemiological studies by properly adjusting for missing data.
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