Captario, Gothenburg, Sweden.
Data Science & AI, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden.
Pharm Stat. 2021 Nov;20(6):1168-1182. doi: 10.1002/pst.2132. Epub 2021 May 17.
When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.
在为新药开发的第三阶段计划的投资和设计做出决策时,来自前两阶段试验的结果是重要的信息来源。然而,只有在第二阶段结果显示出有希望的治疗效果的项目才会被考虑进行第三阶段的投资决策。这意味着,对于那些进行第三阶段研究的项目,潜在的第二阶段估计值受到选择偏差的影响。我们将在本文中基于治疗效果的一系列分布来研究这种选择偏差的性质。我们举例说明了贝叶斯估计的一些特性,提供了对第二阶段估计值的收缩,以抵消选择偏差。我们进一步就先验分布的选择提供了一些经验指导,并对第三阶段计划的投资和规划决策的后果进行了评论。