Informing Reimbursement Decisions Using Cost-Effectiveness Modelling: A Guide to the Process of Generating Elicited Priors to Capture Model Uncertainties

Laura Bojke (), Bogdan Grigore, Dina Jankovic, Jaime Peters, Marta Soares and Ken Stein
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Laura Bojke: University of York
Bogdan Grigore: University of Exeter
Dina Jankovic: University of York
Jaime Peters: University of Exeter
Ken Stein: University of Exeter

PharmacoEconomics, 2017, vol. 35, issue 9, No 2, 867-877

Abstract: Abstract In informing decisions, utilising health technology assessment (HTA), expert elicitation can provide valuable information, particularly where there is a less-developed evidence-base at the point of market access. In these circumstances, formal methods to elicit expert judgements are preferred to improve the accountability and transparency of the decision-making process, help reduce bias and the use of heuristics, and also provide a structure that allows uncertainty to be expressed. Expert elicitation is the process of transforming the subjective and implicit knowledge of experts into their quantifiable expressions. The use of expert elicitation in HTA is gaining momentum, and there is particular interest in its application to diagnostics, medical devices and complex interventions such as in public health or social care. Compared with the gathering of experimental evidence, elicitation constitutes a reasonably low-cost source of evidence. Given its inherent subject nature, the potential biases in elicited evidence cannot be ignored and, due to its infancy in HTA, there is little guidance to the analyst wishing to conduct a formal elicitation exercise. This article attempts to summarise the stages of designing and conducting an expert elicitation, drawing on key literature and examples, most of which are not in HTA. In addition, we critique their applicability to HTA, given its distinguishing features. There are a number of issues that the analyst should be mindful of, in particular the need to appropriately characterise the uncertainty associated with model inputs and the fact that there are often numerous parameters required, not all of which can be defined using the same quantities. This increases the need for the elicitation task to be as straightforward as possible for the expert to complete.

Date: 2017
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DOI: 10.1007/s40273-017-0525-1

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