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A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations

Andrea Gabrio, Michael J. Daniels and Gianluca Baio

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 2, 607-629

Abstract: 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 non‐ignorable missingness 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.

Date: 2020
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