Hoering Antje, Leblanc Mike, Crowley John J
Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Clin Cancer Res. 2008 Jul 15;14(14):4358-67. doi: 10.1158/1078-0432.CCR-08-0288.
Cancer therapies with mechanisms of action which are very different from the more conventional chemotherapies are now being developed. In this article, we investigate the performance of several phase III clinical trial designs, both for testing the overall efficacy of a targeted agent and for testing its efficacy in a subgroup of patients with a tumor marker present. We study different designs and different underlying scenarios assuming continuous markers, and assess the trade-off between the number of patients on the study and the effectiveness of treatment in the subgroup of marker-positive patients.
We investigate binary outcomes and use simulation studies to determine sample size and power for the different designs and the various scenarios. We also simulate marker prevalence and marker misclassification and evaluate their effect on power and sample size.
In general, a targeted design which randomizes patients with the appropriate marker status performs the best in all scenarios with an underlying true predictive marker. Randomizing all patients regardless of their marker values performs as well as or better in most cases than a clinical trial that randomizes the patient to a treatment strategy based on marker value versus standard of care.
If there is the possibility that the new treatment helps marker-negative patients, or that the cutpoint determining marker status has not been well established and the marker prevalence is large enough, we recommend randomizing all patients regardless of marker values, but using a design such that both the overall and the targeted subgroup hypothesis can be tested.
目前正在研发作用机制与传统化疗大不相同的癌症治疗方法。在本文中,我们研究了几种III期临床试验设计的性能,包括测试靶向药物的总体疗效以及在存在肿瘤标志物的患者亚组中测试其疗效。我们研究了假设为连续标志物的不同设计和不同潜在情况,并评估了研究中患者数量与标志物阳性患者亚组中治疗效果之间的权衡。
我们研究二元结果,并使用模拟研究来确定不同设计和各种情况下的样本量和检验效能。我们还模拟了标志物患病率和标志物错误分类,并评估它们对检验效能和样本量的影响。
一般来说,在所有存在潜在真实预测标志物的情况下,将具有适当标志物状态的患者随机分组的靶向设计表现最佳。在大多数情况下,将所有患者无论其标志物值如何进行随机分组的效果与将患者根据标志物值与标准治疗进行随机分组的临床试验相同或更好。
如果新治疗有可能帮助标志物阴性患者,或者确定标志物状态的切点尚未明确且标志物患病率足够高,我们建议将所有患者无论标志物值如何进行随机分组,但采用一种能够同时检验总体假设和靶向亚组假设的设计。