Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland 20892, USA.
Clin Cancer Res. 2010 Jan 15;16(2):691-8. doi: 10.1158/1078-0432.CCR-09-1357. Epub 2010 Jan 12.
Many anticancer therapies benefit only a subset of treated patients and may be overlooked by the traditional broad eligibility approach to design phase III clinical trials. New biotechnologies such as microarrays can be used to identify the patients that are most likely to benefit from anticancer therapies. However, due to the high-dimensional nature of the genomic data, developing a reliable classifier by the time the definitive phase III trail is designed may not be feasible.
Previously, Freidlin and Simon (Clinical Cancer Research, 2005) introduced the adaptive signature design that combines a prospective development of a sensitive patient classifier and a properly powered test for overall effect in a single pivotal trial. In this article, we propose a cross-validation extension of the adaptive signature design that optimizes the efficiency of both the classifier development and the validation components of the design.
The new design is evaluated through simulations and is applied to data from a randomized breast cancer trial.
The cross-validation approach is shown to considerably improve the performance of the adaptive signature design. We also describe approaches to the estimation of the treatment effect for the identified sensitive subpopulation.
许多抗癌疗法仅对一部分接受治疗的患者有效，而传统的广泛纳入标准的设计方法可能会忽略这些疗法。新的生物技术，如微阵列，可以用于识别最有可能从抗癌疗法中获益的患者。然而，由于基因组数据的高维性质，在设计明确的 III 期试验时，开发出可靠的分类器可能并不可行。
此前，弗里德林和西蒙（Clinical Cancer Research，2005）引入了适应性签名设计，该设计将敏感患者分类器的前瞻性开发与单一关键试验中的总体效果的适当有力检验相结合。在本文中，我们提出了适应性签名设计的交叉验证扩展，该扩展优化了设计的分类器开发和验证部分的效率。
新设计通过模拟进行了评估，并应用于随机乳腺癌试验的数据。
交叉验证方法显著提高了适应性签名设计的性能。我们还描述了用于估计鉴定出的敏感亚组的治疗效果的方法。
