  Public policy-makers use cost-effectiveness analyses (CEA) to decide which
health and social care interventions to provide. Appropriate methods have not
been developed for handling missing data in complex settings, such as for CEA
that use data from cluster randomised trials (CRTs). We present a multilevel
multiple imputation (MI) approach that recognises when missing data have a
hierarchical structure, and is compatible with the bivariate multilevel models
used to report cost-effectiveness. We contrast the multilevel MI approach with
single-level MI and complete case analysis in a CEA alongside a CRT. The paper
highlights the importance of adopting a principled approach to handling missing
values in settings with complex data structures.
