1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
2 Department of Mathematics, Chalmers University, Gothenburg, Sweden.
Stat Methods Med Res. 2019 Jul;28(7):2096-2111. doi: 10.1177/0962280217747312. Epub 2017 Dec 18.
Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.
基于贝叶斯决策理论方法,我们优化了针对靶向治疗开发的频繁单阶段和自适应两阶段试验设计,其中除了总体人群外,还研究了一个预先定义的亚组。在这种情况下,可以通过效用函数来量化决策的损失和收益,这些效用函数考虑了不同利益相关者的偏好。特别是,我们从商业赞助商(最大化净现值)和社会(最大化特定人群新治疗方法的成本调整预期健康收益)的角度优化期望效用。我们考虑了具有部分富集的单阶段和自适应两阶段设计,其中从亚组招募的患者比例是一个设计参数。对于自适应设计,我们使用动态规划方法来推导出最优的自适应规则。将提出的设计与非富集试验(即亚组中的患者比例与基础人群中的患病率相对应)进行比较。我们表明,部分富集设计可以显著提高预期效用。此外,自适应部分富集设计比单阶段设计更稳健,即使在基于不同先验的情况下评估预期效用,也能保持较高的预期效用,而该先验与优化中使用的先验不同。此外,我们发现,赞助商效用函数优化的试验与基于社会观点优化的试验相比,样本量更小,并且即使有大量证据表明该疗法仅对亚组有效,也可能包括总体人群(来自亚组的补充患者)。