With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data-based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations. Within GlaxoSmithKline, we have adopted a structured approach to prior elicitation on the basis of the SHELF elicitation framework and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones. The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate. As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company-wide decision making.
In prior works, this group demonstrated the feasibility of valid adaptive sequential designs for crossover bioequivalence studies. In this paper, we extend the prior work to optimize adaptive sequential designs over a range of geometric mean test/reference ratios (GMRs) of 70-143% within each of two ranges of intra-subject coefficient of variation (10-30% and 30-55%). These designs also introduce a futility decision for stopping the study after the first stage if there is sufficiently low likelihood of meeting bioequivalence criteria if the second stage were completed, as well as an upper limit on total study size. The optimized designs exhibited substantially improved performance characteristics over our previous adaptive sequential designs. Even though the optimized designs avoided undue inflation of type I error and maintained power at ≥ 80%, their average sample sizes were similar to or less than those of conventional single stage designs.
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