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Background Missing cost and outcomes data is a common problem for health economists analysing RCTs. Systematic reviews of the economic evaluation literature suggest that many analysts use crude methods (e.g. complete case analysis or single imputation) which are vulnerable to bias and may underestimate uncertainty. Multiple imputation (MI) is more appropriate if data are missing at random, but raises a number of methodological and practical challenges. Two case study RCTs assessing the costeffectiveness of new interventions are used to explore these challenges.

Challenges of implementing multiple imputation to address missing data in economic evaluations