  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.

Methodology;Machine Learning;Applications;Computation