Gerke Oke, Siedentop Harald
Department of Nuclear Medicine, Odense University Hospital, Odense C, Denmark.
Stat Med. 2008 Nov 20;27(26):5329-44. doi: 10.1002/sim.3037.
In phase I oncology trials conducted over the past few decades, the maximum tolerated dose (MTD) has usually been estimated by the traditional escalation rule (TER), which traces back to 1973. In the meantime, new methods have been proposed which hope to estimate the true MTD more precisely than the TER while using less patients. In this simulation study, TER is compared with the accelerated titration dose design (ATD), two up-and-down designs (biased coin design, r-in-a-row (RIAR)), the maximum likelihood version of the continual reassessment method (CRML), and a Bayesian method that is implemented in the software Bayesian ADEPT (assisted decision-making in early phase trials). Each design was applied to 50,000 simulated studies. The designs were then compared for accuracy in detecting the true MTD (which is known here), while taking into account the average number of patients and toxicities per run. In terms of accuracy, ADEPT outperformed the other methods in the scenario with medium toxicity and was close to the best methods in the low and high toxic scenarios. The average number of patients needed per run was the lowest for TER in the scenario with low toxicity and for ADEPT in the remaining scenarios. The longer the escalation path to the target region of the MTD, the more the difference in the average number of patients per run pronounced between TER and ADEPT. TER induced least toxicities in all scenarios. ADEPT turned out to be quick and accurate in determining the MTD, while TER was the safest but least accurate method. CRML was as accurate as TER, and the up-and-down designs did not excel. Bayesian ADEPT is considered a valuable tool for the conduct of phase I dose-escalation trials in oncology, but careful preparation is indispensable before its practical use.
在过去几十年进行的肿瘤学I期试验中，最大耐受剂量（MTD）通常通过可追溯到1973年的传统递增规则（TER）来估计。与此同时，人们提出了一些新方法，希望能比TER更精确地估计真实的MTD，同时使用更少的患者。在这项模拟研究中，将TER与加速滴定剂量设计（ATD）、两种上下设计（偏倚硬币设计、连续r次成功（RIAR））、连续重新评估方法的最大似然版本（CRML）以及软件贝叶斯ADEPT（早期试验中的辅助决策）中实现的贝叶斯方法进行了比较。每种设计都应用于50000项模拟研究。然后比较这些设计在检测真实MTD（此处已知）方面的准确性，同时考虑每次试验的平均患者数量和毒性。在准确性方面，在中度毒性的情况下，ADEPT优于其他方法，在低毒性和高毒性情况下接近最佳方法。在低毒性情况下，每次试验所需的平均患者数量TER最少，在其余情况下ADEPT最少。达到MTD目标区域的递增路径越长，TER和ADEPT每次试验的平均患者数量差异就越明显。TER在所有情况下产生的毒性最小。结果表明，ADEPT在确定MTD方面快速且准确，而TER是最安全但最不准确的方法。CRML与TER一样准确，上下设计并不出色。贝叶斯ADEPT被认为是肿瘤学I期剂量递增试验的一个有价值的工具，但在实际使用之前，仔细准备是必不可少的。
