Translation of innovative designs into phase I trials
Translation of innovative designs into phase I trials
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DOI：
10.1200/jco.2007.12.1012
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发表时间：
2007-11-01
影响因子：
45.3
通讯作者：
Porter, Alan
中科院分区：
文献类型：
--
作者：
Rogatko, Andre;Schoeneck, David;Porter, Alan
目的新的抗癌疗法的I期临床试验确定进一步测试的合适剂量。他们的设计优化是至关重要的，因为他们招募癌症患者的福祉是明显处于危险之中。本研究探讨更有效的统计设计，以临床practice.Methods的知识转移的有效性，我们检查了癌症I期试验的科学引文索引数据库在1991年和2006年之间的摘要记录，并将其分类为临床（剂量发现试验）和统计试验（剂量递增设计的方法学研究）。然后，我们映射这两组跟踪试验采用新的统计designs.Results一千二百三十五临床和90统计研究。只有1.6%的I期癌症试验（1,235项试验中的20项）遵循了其中一项统计研究中提出的设计。这20项临床研究显示，统计论文的发表与其转化为临床论文之间存在广泛的滞后。这20项临床试验遵循贝叶斯自适应设计。其余使用的变化标准的上升和下降的methods.Conclusion的一个后果，使用不太有效的设计是，更多的患者治疗剂量的治疗窗口之外。模拟研究表明，升降设计在最佳剂量水平下仅治疗了35%的患者，而贝叶斯自适应设计为55%。这意味着治疗效果的不必要损失，可能还有生命损失。我们建议监管机构（如美国食品药品监督管理局）应积极鼓励采用统计设计，使更多的患者在接近最佳剂量下接受治疗，同时控制过度毒性。
Purpose Phase I clinical trials of new anticancer therapies determine suitable doses for further testing. Optimization of their design is vital in that they enroll cancer patients whose well-being is distinctly at risk. This study examines the effectiveness of knowledge transfer about more effective statistical designs to clinical practice.Methods We examined abstract records of cancer phase I trials from the Science Citation Index database between 1991 and 2006 and classified them into clinical (dose-finding trials) and statistical trials (methodologic studies of dose-escalation designs). We then mapped these two sets by tracking which trials adopted new statistical designs.Results One thousand two hundred thirty-five clinical and 90 statistical studies were identified. Only 1.6% of the phase I cancer trials (20 of 1,235 trials) followed a design proposed in one of the statistical studies. These 20 clinical studies showed extensive lags between publication of the statistical paper and its translation into a clinical paper. These 20 clinical trials followed Bayesian adaptive designs. The remainder used variations of the standard up-and-down method.Conclusion A consequence of using less effective designs is that more patients are treated with doses outside the therapeutic window. Simulation studies have shown that up-and-down designs treated only 35% of patients at optimal dose levels versus 55% for Bayesian adaptive designs. This implies needless loss of treatment efficacy and, possibly, lives. We suggest that regulatory agencies (eg, US Food and Drug Administration) should proactively encourage the adoption of statistical designs that would allow more patients to be treated at near-optimal doses while controlling for excessive toxicity.