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Trial-based economic evaluations are typically performed on cross-sectionalvariables, derived from the responses for only the completers in the study,using methods that ignore the complexities of utility and cost data (e.g.skewness and spikes). We present an alternative and more efficient Bayesianparametric approach to handle missing longitudinal outcomes in economicevaluations, while accounting for the complexities of the data. We specify aflexible parametric model for the observed data and partially identify thedistribution of the missing data with partial identifying restrictions andsensitivity parameters. We explore alternative nonignorable scenarios throughdifferent priors for the sensitivity parameters, calibrated on the observeddata. Our approach is motivated by, and applied to, data from a trial assessingthe cost-effectiveness of a new treatment for intellectual disability andchallenging behaviour.

A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations