Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

Abstract

Background: Empirical evidence supporting the cost-effectiveness estimates of particular health care technologies may be limited, or it may even be missing entirely. In these situations, additional information, often in the form of expert judgments, is needed to reach a decision. There are formal methods to quantify experts' beliefs, termed as structured expert elicitation (SEE), but only limited research is available in support of methodological choices. Perhaps as a consequence, the use of SEE in the context of cost-effectiveness modelling is limited.

Objectives: This article reviews applications of SEE in cost-effectiveness modelling with the aim of summarizing the basis for methodological choices made in each application and recording the difficulties and challenges reported by the authors in the design, conduct, and analyses.

Methods: The methods used in each application were extracted along with the criteria used to support methodological and practical choices and any issues or challenges discussed in the text. Issues and challenges were extracted using an open field, and then categorised and grouped for reporting.

Results: The review demonstrates considerable heterogeneity in methods used, and authors acknowledge great methodological uncertainty in justifying their choices. Specificities of the context area emerging as potentially important in determining further methodological research in elicitation are between- expert variation and its interpretation, the fact that substantive experts in the area may not be trained in quantitative subjects, that judgments are often needed on various parameter types, the need for some form of assessment of validity, and the need for more integration with behavioural research to devise relevant debiasing strategies.

Conclusions: This review of experiences of SEE highlights a number of specificities/constraints that can shape the development of guidance and target future research efforts in this area.

Keywords: Bayesian; cost effectiveness; decision modeling; elicitation; expert judgment; subjective.