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

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

机构信息

Global Drug Development, Novartis Pharma AG, Basel, Switzerland.

Department of Mathematical Sciences, Durham University, Durham, UK.

出版信息

Pharm Stat. 2022 Sep;21(5):1005-1021. doi: 10.1002/pst.2212. Epub 2022 Apr 3.

Abstract

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.

摘要

制药公司经常需要根据早期临床试验的有限知识来决定药物开发项目。在这种情况下,征求专家的意见是综合未知感兴趣数量证据的一种有吸引力的方法。在计算药物开发项目的成功概率时,多个感兴趣的数量——例如药物对不同终点的影响——不应被视为不相关。我们讨论了在谢菲尔德启发框架(SHELF)内为几个相关数量建立多元分布的两种方法。第一种方法是根据对另一个数量的了解来启发专家对感兴趣数量的判断。对于第二种方法,我们首先为每个感兴趣的数量引出边际分布。然后,对于每一对数量,我们引出两者都位于各自引出的中位数同一侧的一致性概率。这允许我们指定一个 Copula 来获得感兴趣数量的联合分布。我们展示了这些方法如何在一个启发式研讨会中使用,该研讨会是为了评估哮喘药物注册计划的成功概率而进行的。在关键研究完成之前获得的专家判断与最终试验结果非常吻合。

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