A Bayesian modelling framework for health care resource use and costs in trial-based economic evaluations

TL;DR

This paper introduces a Bayesian modeling framework for analyzing healthcare resource use and costs in trial-based economic evaluations, especially handling partially observed data to improve validity and account for uncertainty.

Contribution

It proposes a flexible Bayesian approach for modeling incomplete HRU data, addressing limitations of ad-hoc methods and enhancing analysis accuracy in economic evaluations.

Findings

Bayesian framework effectively models partially observed HRU data.

Compared to traditional methods, it reduces bias and improves uncertainty quantification.

Application to real data demonstrates practical advantages of the approach.

Abstract

Individual-level effectiveness and healthcare resource use (HRU) data are routinely collected in trial-based economic evaluations. While effectiveness is often expressed in terms of utility scores derived from some health-related quality of life instruments (e.g.~EQ-5D questionnaires), different types of HRU may be included. Costs are usually generated by applying unit prices to HRU data and statistical methods have been traditionally implemented to analyse costs and utilities or after combining them into aggregated variables (e.g. Quality-Adjusted Life Years). When outcome data are not fully observed, e.g. some patients drop out or only provided partial information, the validity of the results may be hindered both in terms of efficiency and bias. Often, partially-complete HRU data are handled using "ad-hoc" methods, implicitly relying on some assumptions (e.g. fill-in a zero) which are hard to justify beside the practical convenience of increasing the completion rate. We present a general Bayesian framework for the modelling of partially-observed HRUs which allows a flexible model specification to accommodate the typical complexities of the data and to quantify the impact of different types of uncertainty on the results. We show the benefits of using our approach using a motivating example and compare the results to those from traditional analyses focussed on the modelling of cost variables after adopting some ad-hoc imputation strategy for HRU data.