Adaptive designs for subpopulation analysis optimizing utility functions.
Adaptive designs for subpopulation analysis optimizing utility functions.
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DOI：
10.1002/bimj.201300257
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发表时间：
2015-01
影响因子：
1.7
通讯作者：
Koenig, Franz
中科院分区：
文献类型：
--
作者：
Graf, Alexandra C.;Posch, Martin;Koenig, Franz
如果对治疗的反应取决于遗传生物标志物，则重要的是确定预测性生物标志物，其定义治疗具有积极获益风险平衡的（亚）人群。确定相关亚群的一种方法是亚组分析，其中在生物标志物阳性和生物标志物阴性组中估计治疗效果。亚组分析具有挑战性，因为几种类型的风险与亚组的推断有关。一方面，如果忽略相关亚群，则可能会由于稀释整个人群的治疗效果而错过治疗选择。此外，即使稀释的治疗效果可以在整个人群中得到证明，但在可以提前确定患者时，治疗无法从治疗中获益的患者也是不道德的。另一方面，选择虚假亚群增加了将有效治疗限制在潜在受益人群的过窄部分的风险。我们建议用效用函数量化这些风险，并研究非适应性研究设计，这些设计允许使用多个检验程序以及适应性设计对亚组进行推断，其中亚组可能在中期分析中被选择。这种自适应和非自适应设计的特点进行了比较的范围内的情况。
If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.
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