Wang Sue-Jane
Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Maryland, USA.
J Formos Med Assoc. 2008 Dec;107(12 Suppl):19-27. doi: 10.1016/s0929-6646(09)60005-x.
In the early to late phases of conventional clinical trials, improvement of disease status at study baseline is the anchor of an effective treatment measured by therapeutic response. These population-based clinical trials do not formally account for disease-associated marker genotype or genome-associated therapeutic response. We discuss alternative study designs in pharmacogenomic or pharmacogenetic clinical trials for genomic or genetic biomarker development, and for formally assessing the clinical utility of genomic or genetic (composite) biomarkers. A two-stage adaptive strategy from completed, ongoing or prospectively planned pharmacogenomic or pharmacogenetic clinical trials is described for development of a genomic or genetic biomarker. We present two types of adaptive design: (1) the genomic biomarker is developed external to the clinical trial, which is designed for treatment effect inference; and (2) first-stage data are used to explore a genomic biomarker, but statistical inference of treatment effect in the genomically or genetically defined biomarker subset is only performed at the second stage of the same trial. When the null hypothesis of no treatment effect in all randomized patients and the genomic patient subset are prospectively specified, we compare the statistical power between fixed and adaptive designs. We also compare the two types of adaptive design. Results from simulation studies showed that adaptive design is more powerful than fixed design for those genomic or genetic biomarkers whose clinical utility is predictive of treatment effect. Pursuit of adaptive design gains at least 20% to more than 30% genomic patient subset power when the genomic biomarker status is readily usable at study initiation, in comparison to when it is explored using the first-stage data of the same clinical trial. In exploratory studies, adaptive strategy provides wide flexibility in the process of genomic or genetic biomarker development. In contrast, an adaptive design trial that employs limited flexibility, and is an adequate and well-controlled investigation, has a greater power gain than a fixed design trial, in which the genomic biomarker is capable of predicting treatment effects that pertain only to the prespecified genomic or genetic patient subset.
在传统临床试验的早期到晚期阶段,研究基线时疾病状态的改善是通过治疗反应衡量的有效治疗的关键。这些基于人群的临床试验并未正式考虑疾病相关标志物基因型或基因组相关治疗反应。我们讨论了药物基因组学或药物遗传学临床试验中用于基因组或遗传生物标志物开发以及正式评估基因组或遗传(复合)生物标志物临床效用的替代研究设计。描述了一种来自已完成、正在进行或前瞻性规划的药物基因组学或药物遗传学临床试验的两阶段适应性策略,用于开发基因组或遗传生物标志物。我们提出了两种类型的适应性设计:(1)基因组生物标志物在临床试验外部开发,该试验旨在进行治疗效果推断;(2)第一阶段数据用于探索基因组生物标志物,但仅在同一试验的第二阶段对基因组或基因定义的生物标志物亚组进行治疗效果的统计推断。当预先指定所有随机分组患者以及基因组患者亚组中无治疗效果的零假设时,我们比较固定设计和适应性设计之间的统计效能。我们还比较了两种类型的适应性设计。模拟研究结果表明,对于那些临床效用可预测治疗效果的基因组或遗传生物标志物而言,适应性设计比固定设计更具效能。与使用同一临床试验的第一阶段数据进行探索相比,当在研究开始时基因组生物标志物状态易于使用时,采用适应性设计可使基因组患者亚组的效能至少提高20%至超过30%。在探索性研究中,适应性策略在基因组或遗传生物标志物开发过程中提供了广泛的灵活性。相比之下,采用有限灵活性且是充分且良好对照研究的适应性设计试验,比固定设计试验具有更大的效能提升,在固定设计试验中,基因组生物标志物仅能预测与预先指定的基因组或基因患者亚组相关的治疗效果。