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Missing Data in Trial-Based Cost-Effectiveness Analysis: The Journey Continues
Jiunn Wang1, Baptiste Leurent2, Manuel Gomes3
1Department of Primary Care and Population Health, University College London, London, UK. jiunn.wang@ucl.ac.uk.
Pharmacoeconomics
|August 12, 2026
Summary
Missing data are common in cost-effectiveness analyses (CEAs), often biasing results. While multiple imputation is increasing, complete case analysis remains prevalent, indicating a slow adoption of best practices for handling missing data in health economics research.
Area of Science:
- Health Economics
- Biostatistics
- Clinical Trials
Background:
- Missing data are prevalent in trial-based cost-effectiveness analyses (CEAs), potentially leading to biased estimates.
- Previous reviews indicated inconsistent application of appropriate statistical methods for handling missing data in CEAs.
- Despite published methodological guidelines, the impact on current practices remains unclear.
Purpose of the Study:
- To provide a contemporary overview of missing data handling methods in UK-based trial-based CEAs.
- To assess the extent to which published guidance on missing data has influenced current research practices.
Main Methods:
- A systematic review of trial-based CEAs published in the Health Technology Assessment journal from 2022 to 2024.
- Identification and analysis of 63 relevant studies to evaluate their approaches to missing data.
Main Results:
- Missing data are pervasive, with a median of 61% of individuals having complete cost-effectiveness data.
- Adoption of multiple imputation has increased, but complete case analysis is still widely used.
- While 60% of studies performed sensitivity analyses, only 10% explored departures from the missing-at-random assumption.
Conclusions:
- The adoption of appropriate missing data methods in trial-based CEAs is progressing slowly despite available resources.
- Improved transparency and practice require routine reporting of key information regarding missing data.
- Recommendations include reporting data completeness, reasons for missingness, assumptions, methods, and sensitivity analyses.
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