Biometric Research Branch, National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA.
Stat Med. 2012 May 10;31(10):901-14. doi: 10.1002/sim.4462. Epub 2012 Jan 11.
Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker.
大多数肿瘤学的新药研发都是基于针对特定分子。基因组图谱和失调的药物靶点因患者而异，使得新的治疗方法可能仅有益于传统上归入同一临床试验的一部分患者。正在开发预测性生物标志物来识别最有可能从特定治疗中受益的患者；然而，它们的生物学基础并不总是明确的。因此，为了更全面地评估治疗效果，有必要将标志物阴性患者纳入试验。在本文中，我们提出了一种两阶段贝叶斯设计，该设计将标志物阳性和标志物阴性患者都纳入临床试验中。我们提出了一系列先验分布，代表了对预测生物标志物的先验置信度的程度。为了避免让患者接受可能无法受益的治疗，我们进行了中期分析，这可能会停止招募标志物阴性患者或所有患者。我们通过模拟证明，所使用的设计和先验可以控制 I 型错误，提供足够的功效，并使基于生物标志物证据强度的先验规范对阴性测试患者的早期无效性分析成为可能。
