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.
翻译:制药公司经常需要根据早期临床试验的有限知识就药物开发方案作出决定。在这种情况下,专家的判断是一种具有吸引力的方法,可以综合无名利息数量的证据。在计算药物开发方案成功概率时,不应将多种利息数量(如药物对不同终点的影响)视为无关紧要。我们讨论了在Sheffield ELIation框架(SHELF)内为若干相关数量建立多变分配办法的两种办法。第一种办法是让专家对一定数量的利息做出判断,但以对另一个项目的了解为条件。第二种办法是,我们首先为每一数量的利益进行边际分配。然后,对于每对数量,我们得出一致的概率,即两者都位于各自获得的中值的同一一边。这使我们能够指定一个混合体,以获得利息数量的联合分配。我们展示了这些办法是如何在评估哮喘药物注册方案成功率的咨询讲习班上使用的。在完成关键研究之前获得的最后结果时,专家的判断与最后结果一致。