Bayesian quantitative decision making incorporating historical data in rare disease drug development

Music City Center 

Evidence-based decision-making is crucial at every stage of clinical development. In common diseases, phase II proof-of-concept (PoC) studies play a vital role as gatekeepers to increase the efficiency of asset selection for late-stage development by guiding early decisions to terminate the development of ineffective assets and accelerate the development of promising ones. The process of developing Go/No-Go decision criteria and examining the study operating characteristics is called quantitative decision-making (QDM) assessment which triggers cross-functional discussions about optimality of the entire clinical development plan. However, in rare disease drug development, phase II PoC studies are often small or even infeasible due to the rarity of target diseases, and thus the PoC declaration is frequently addressed by insufficiently informative data, which leads to high probabilities of inconclusive results and undermines the ability to make definitive Go/No-Go decisions. A potential solution to the QDM assessment with limited sample size is to utilize a Bayesian framework for incorporating existing prior information such as data from previously completed clinical trials and real-world data. We introduce a technique of Bayesian information borrowing into the QDM assessment via power prior, allowing for the incorporation of historical data, which makes the Go/No-Go decision reliable even with the limited amount of PoC data. For instance, data from dose-escalation cohorts can be leveraged to augment preliminary efficacy data of the treatment, and furthermore historical trial data or real-world data can serve as external controls when concurrent controls are not available. Through a simulation study, we present the operating characteristics of the Bayesian QDM with information borrowing.

Quantitative decision making

Go/No-Go decision

Bayesian information borrowing

Rare disease drug development