A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.

本研究提出一种“逆向”贝叶斯方法，助力生物医药申办方基于最终疗效或安全性目标制定早期研发阶段的放行/否决（Go/No-Go）决策标准——此类目标在III期临床试验（Phase 3）中通常更为清晰明确。本研究从III期临床试验的成功定义出发，结合先验信息与后续研发阶段的成本，推导得到以下要素间的定量关系：既往与当前研究结果、研究设计（如样本量、研究周期或给药剂量）、真实效应、目标成功概率（Probability of Success，简称PoS）、预期经济损失，以及终止潜在有效管线资产的预期概率。本文以描述体重减轻轨迹的指数模型为例，演示如何达成上述研究目标，并将预期损失与终止有价值候选化合物的概率随不同决策标准的变化情况进行可视化呈现。申办方可基于自身对研发目标的耐受度优化放行/否决决策标准。该方法还可推广至其他非线性模型。本研究的一项附带成果是，通过明确列出早期内部决策过程中所需的详尽信息与假设条件，凸显了传统主观直觉决策法的局限性。本文补充材料可在线获取。