Bayesian models for cost-effectiveness analysis in the presence of structural zero costs

TL;DR

This paper develops a Bayesian modeling framework for cost-effectiveness analysis that accounts for skewed cost data and structural zeros, improving accuracy in health economic evaluations.

Contribution

It introduces a comprehensive Bayesian hurdle model for cost-effectiveness data, incorporating selection, marginal, and conditional models to handle zero costs and skewed distributions.

Findings

Effective modeling of zero-inflated cost data

Improved accuracy in cost-effectiveness estimates

Flexible Bayesian framework for complex data structures

Abstract

Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking the suitable measure of clinical benefit (\eg QALYs) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level datasets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian model which consists of a selection model for the subjects with null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (conditionally on the observed costs). The model is presented using a working example to describe its main features.