Uncertainty representation for early phase clinical test evaluations: a case study

TL;DR

This paper demonstrates how Bayesian methods, including graphical models and uncertainty analysis, can improve early evaluation of diagnostic tests with limited data, aiding decision-making in healthcare technology development.

Contribution

It introduces a Bayesian framework using influence diagrams and elicitation to assess uncertainty in early clinical test evaluations, exemplified by a COPD diagnostic test case.

Findings

Uncertainty analysis showed test value was robust despite data limitations.

Graphical models facilitated integration of care pathway information.

Bayesian approach enhanced decision support in early test evaluation.

Abstract

In early clinical test evaluations the potential benefits of the introduction of a new technology into the healthcare system are assessed in the challenging situation of limited available empirical data. The aim of these evaluations is to provide additional evidence for the decision maker, who is typically a funder or the company developing the test, to evaluate which technologies should progress to the next stage of evaluation. In this paper we consider the evaluation of a diagnostic test for patients suffering from Chronic Obstructive Pulmonary Disease (COPD). We describe the use of graphical models, prior elicitation and uncertainty analysis to provide the required evidence to allow the test to progress to the next stage of evaluation. We specifically discuss inferring an influence diagram from a care pathway and conducting an elicitation exercise to allow specification of prior distributions over all model parameters. We describe the uncertainty analysis, via Monte Carlo simulation, which allowed us to demonstrate that the potential value of the test was robust to uncertainties. This paper provides a case study illustrating how a careful Bayesian analysis can be used to enhance early clinical test evaluations.