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Public policy-makers use cost-effectiveness analyses (CEA) to decide whichhealth and social care interventions to provide. Appropriate methods have notbeen developed for handling missing data in complex settings, such as for CEAthat use data from cluster randomised trials (CRTs). We present a multilevelmultiple imputation (MI) approach that recognises when missing data have ahierarchical structure, and is compatible with the bivariate multilevel modelsused to report cost-effectiveness. We contrast the multilevel MI approach withsingle-level MI and complete case analysis in a CEA alongside a CRT. The paperhighlights the importance of adopting a principled approach to handling missingvalues in settings with complex data structures.

Handling missing values in cost-effectiveness analyses that use data from cluster randomised trials