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A Scoping Review of Item-Level Missing Data in Within-Trial Cost-Effectiveness Analysis
Xiaoxiao Ling1, Andrea Gabrio2, Alexina Mason3
1Department of Statistical Science, University College London, London, England, UK.
Summary
Missing data in cost-effectiveness analyses (CEA) are handled differently for costs and quality of life (QOL). Imputation methods vary by data type and aggregation level, impacting CEA decision-making clarity.
Area of Science:
- Health Economics
- Clinical Trial Methodology
- Biostatistics
Background:
- Cost-effectiveness analysis (CEA) often uses self-reported questionnaires, which are susceptible to missing data.
- Handling missing item-level data is crucial for reliable trial-based CEAs.
Purpose of the Study:
- To review methods for handling missing multi-item questionnaire data in trial-based CEAs.
- To identify common practices and variations in data imputation for costs and quality of life (QOL).
Main Methods:
- Systematic review of trial-based CEAs published between January 2016 and April 2021.
- Data extraction focused on missing data handling, imputation methods, and aggregation levels.
Main Results:
- 87 trial-based CEAs were analyzed.
- Complete case analysis and multiple imputation (MI) were common for base-case analyses.
- Complete case analysis dominated sensitivity analyses; imputation levels differed for costs (item-level) and QOL (time-point level).
Conclusions:
- Missing costs and QOL data are imputed at different levels in current CEAs.
- The impact of varying imputation strategies on CEA outcomes remains unclear due to limited reporting.
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