Abstract
Cost-effectiveness analyses (CEAs) alongside longitudinal randomised controlled trials (RCTs) often use multi-item questionnaires to collect data at multiple follow-ups, and are generally characterised by non-negligible proportions of incomplete outcome data. As routine trial-based CEAs typically aggregate costs and effects data into cross-sectional measures, researchers are motivated to apply standard missing data methods (e.g. multiple imputation) at the most aggregated level (i.e. total costs or QALYs) rather than the disaggregated level (i.e.
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