Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA and Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Biostatistics. 2021 Apr 10;22(2):298-314. doi: 10.1093/biostatistics/kxz031.
Candidate biomarkers discovered in the laboratory need to be rigorously validated before advancing to clinical application. However, it is often expensive and time-consuming to collect the high quality specimens needed for validation; moreover, such specimens are often limited in volume. The Early Detection Research Network has developed valuable specimen reference sets that can be used by multiple labs for biomarker validation. To optimize the chance of successful validation, it is critical to efficiently utilize the limited specimens in these reference sets on promising candidate biomarkers. Towards this end, we propose a novel two-stage validation strategy that partitions the samples in the reference set into two groups for sequential validation. The proposed strategy adopts the group sequential testing method to control for the type I error rate and rotates group membership to maximize the usage of available samples. We develop analytical formulas for performance parameters of this strategy in terms of the expected numbers of biomarkers that can be evaluated and the truly useful biomarkers that can be successfully validated, which can provide valuable guidance for future study design. The performance of our proposed strategy for validating biomarkers with respect to the points on the receiver operating characteristic curve are evaluated via extensive simulation studies and compared with the default strategy of validating each biomarker using all samples in the reference set. Different types of early stopping rules and boundary shapes in the group sequential testing method are considered. Compared with the default strategy, our proposed strategy makes more efficient use of the limited resources in the reference set by allowing more candidate biomarkers to be evaluated, giving a better chance of having truly useful biomarkers successfully validated.
候选生物标志物在实验室中被发现后，需要经过严格的验证才能应用于临床。然而，收集验证所需的高质量标本通常既昂贵又耗时；此外，此类标本的数量通常有限。早期检测研究网络已经开发了有价值的标本参考集，多个实验室可以用于验证生物标志物。为了优化成功验证的机会，至关重要的是要有效地利用这些参考集中有限的标本，对有前途的候选生物标志物进行验证。为此，我们提出了一种新颖的两阶段验证策略，将参考集中的样本分为两组进行顺序验证。该策略采用分组序贯检验方法来控制Ⅰ类错误率，并通过轮流分组的方式来最大化可用样本的使用效率。我们根据可评估的生物标志物数量和可成功验证的真正有用的生物标志物数量，开发了该策略的性能参数的分析公式，为未来的研究设计提供了有价值的指导。通过广泛的模拟研究，评估了我们提出的策略在验证生物标志物方面的性能，与使用参考集中所有样本验证每个生物标志物的默认策略进行了比较。考虑了分组序贯检验方法中的不同类型的提前停止规则和边界形状。与默认策略相比，我们的策略通过允许更多的候选生物标志物进行评估，更有效地利用了参考集中的有限资源，从而增加了真正有用的生物标志物成功验证的机会。
