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

TL;DR

This paper introduces a Bayesian parametric method for handling missing longitudinal outcome data in health economic evaluations, accounting for data complexities like skewness and spikes, and exploring nonignorable missing data scenarios.

Contribution

It proposes a flexible Bayesian model that incorporates partial identification and sensitivity analysis for missing data in economic evaluations, improving upon traditional methods.

Findings

Applied to a trial on intellectual disability treatment, demonstrating the method's practical utility.

Showed that accounting for data complexities and missing data mechanisms affects cost-effectiveness conclusions.

Provided a framework for sensitivity analysis in the presence of nonignorable missing data.

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 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.