  Trial-based economic evaluations are typically performed on cross-sectional
variables, 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 Bayesian
parametric approach to handle missing longitudinal outcomes in economic
evaluations, while accounting for the complexities of the data. We specify a
flexible parametric model for the observed data and partially identify the
distribution of the missing data with partial identifying restrictions and
sensitivity parameters. We explore alternative nonignorable scenarios through
different priors for the sensitivity parameters, calibrated on the observed
data. Our approach is motivated by, and applied to, data from a trial assessing
the cost-effectiveness of a new treatment for intellectual disability and
challenging behaviour.
