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Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical
Manuel Gomes1, Karla Díaz-Ordaz1, Richard Grieve1
1Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (MG, KD, RG).
Summary
Multilevel multiple imputation (MI) accurately handles missing data in cost-effectiveness analyses (CEAs) using cluster randomized trials (CRTs). This method is superior to single-level MI and complete case analysis, especially with missing at random or not at random data.
Area of Science:
- Health Economics
- Biostatistics
- Clinical Trial Methodology
Background:
- Handling missing data in cost-effectiveness analyses (CEAs) is crucial for accurate results.
- Cluster randomized trials (CRTs) introduce hierarchical data structures that complicate standard statistical approaches.
- Existing methods like complete case analysis (CCA) and single-level multiple imputation (MI) may not adequately account for this clustering.
Purpose of the Study:
- To contrast a multilevel MI approach, which accounts for data hierarchy, with single-level MI and CCA in CEAs using CRTs.
- To evaluate the performance of these methods under various missing data scenarios, including different proportions and missingness mechanisms (MCAR, MAR, MNAR).
Main Methods:
- A multilevel MI approach compatible with multilevel analytical models was considered for CEAs using CRTs.
- Fully observed data from a CEA of an intervention for active labor diagnosis in primiparous women (using a CRT) were utilized.
- Scenarios with varying proportions (10%-50%) and predictors (individual, cluster-level) of missing costs and outcomes were simulated under MCAR, MAR, and MNAR conditions.
Main Results:
- When data were missing completely at random (MCAR), all methods yielded similar incremental net benefit (INB) estimates.
- Complete case analysis (CCA) estimates diverged from true values when data were missing at random (MAR).
- Multilevel MI consistently provided point estimates and standard errors closer to the true values than single-level MI, across all MAR and missing not at random (MNAR) settings.
Conclusions:
- Multilevel MI effectively accommodates the hierarchical structure inherent in CRTs for cost-effectiveness analyses.
- This approach yields accurate cost-effectiveness estimates across diverse missing data situations, including MAR and MNAR.
- Multilevel MI is recommended for handling missing data in CEAs that employ cluster randomized trials.
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