Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Clin Trials. 2010 Oct;7(5):546-56. doi: 10.1177/1740774510372657. Epub 2010 Jun 22.
Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. Targeted therapies, however, have created major challenges in the design, conduct, and analysis of clinical trials. In traditional clinical trials, treatment effects for various biomarkers are typically evaluated in an exploratory fashion and only limited information about the predictive values of biomarkers obtained.
New study designs are required, which effectively evaluate both the diagnostic and the therapeutic implication of biomarkers.
The Bayesian approach provides a useful framework for optimizing the clinical trial design by directly integrating information about biomarkers and clinical outcomes as they become available. We propose a Bayesian covariate-adjusted response-adaptive randomization design, which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach.
A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. The simulation studies show that the proposed design efficiently identifies patients who benefit most from a targeted therapy and that there are substantial savings in the sample size requirements when compared to alternative designs.
The design does not control for the type I error in the traditional sense and a positive result should be confirmed by conducting an independent phase III study focusing on the selected biomarker profile groups.
We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.
靶向治疗在治疗各种疾病方面变得越来越重要。生物标志物是靶向治疗的关键组成部分,因为它们可以用于识别更有可能从治疗中获益的患者。然而,靶向治疗在临床试验的设计、实施和分析方面带来了重大挑战。在传统的临床试验中,通常以探索性的方式评估各种生物标志物的治疗效果,并且仅获得关于生物标志物预测值的有限信息。
需要新的研究设计,这些设计能够有效地评估生物标志物的诊断和治疗意义。
贝叶斯方法通过直接整合关于生物标志物和临床结果的信息,为优化临床试验设计提供了有用的框架,这些信息在可用时会被整合。我们提出了一种贝叶斯协变量调整的基于反应的自适应随机化设计,该设计利用个体生物标志物谱和患者的临床结果,在试验过程中为每个患者分配最有效的治疗方法。使用偏最小二乘回归方法自适应地确定预测性生物标志物亚组。
进行了一系列模拟研究,以检查所提出的研究设计的操作特征。模拟研究表明,该设计能够有效地识别最受益于靶向治疗的患者,与替代设计相比,样本量需求有大幅度的节省。
该设计没有以传统意义上控制 I 型错误,阳性结果应该通过进行关注选定生物标志物谱组的独立 III 期研究来确认。
我们得出结论,所提出的设计可能在靶向治疗开发的早期疗效阶段发挥有用的作用。