Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study

Pharmaceutical companies regularly need to make decisions about drug development programs based on the limited knowledge from early stage clinical trials. In this situation, eliciting the judgements of experts is an attractive approach for synthesising evidence on the unknown quantities of interest. When calculating the probability of success for a drug development program, multiple quantities of interest-such as the effect of a drug on different endpoints-should not be treated as unrelated. We discuss two approaches for establishing a multivariate distribution for several related quantities within the SHeffield ELicitation Framework (SHELF). The first approach elicits experts' judgements about a quantity of interest conditional on knowledge about another one. For the second approach, we first elicit marginal distributions for each quantity of interest. Then, for each pair of quantities, we elicit the concordance probability that both lie on the same side of their respective elicited medians. This allows us to specify a copula to obtain the joint distribution of the quantities of interest. We show how these approaches were used in an elicitation workshop that was performed to assess the probability of success of the registrational program of an asthma drug. The judgements of the experts, which were obtained prior to completion of the pivotal studies, were well aligned with the final trial results.

Automated summary

Pharmaceutical companies often need to make decisions about drug development programs based on limited knowledge from early stage clinical trials. This article discusses the use of expert judgements to synthesize evidence on unknown quantities of interest. Two approaches within the SHELF framework are presented for establishing a multivariate distribution for related quantities. The article also demonstrates how these approaches were used in assessing the probability of success for an asthma drug's registrational program, with the experts' judgements aligning well with the final trial results.