Division of Translational Research & Applied Statistics, Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
Clin Trials. 2018 Oct;15(5):524-529. doi: 10.1177/1740774518792258. Epub 2018 Aug 13.
Background/aims In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of "model-assisted" designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods. Methods The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway-Dunbar-Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals. Results The Conaway-Dunbar-Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway-Dunbar-Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway-Dunbar-Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years. Conclusion The Conaway-Dunbar-Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway-Dunbar-Peddada method, and the availability of user-friendly software, will augment its use in practice.
背景/目的：在进行 I 期临床试验时，实践中对创新基于模型的设计的有限应用导致了一类“模型辅助”设计的引入，目的是有效地平衡设计的简单性和性能之间的权衡。在这些设计最近激增之前，已经提出了基于等渗毒性概率估计将患者分配给剂量的方法。与模型辅助方法一样，等渗设计允许研究人员避免与基于模型的设计的预试验参数规范相关的困难。本工作的目的是根据当前模型辅助方法的情况，重新审视等渗设计。方法：2004 年，Conaway、Dunbar 和 Peddada 提出了等渗 I 期方法，主要被视为药物组合中的剂量发现设计。在单药治疗中，它在很大程度上被忽视了。鉴于其在更复杂的剂量发现问题（如药物组合和患者异质性）中的强大模拟性能，以及最近开发的伴随该方法的用户友好型软件，我们重新审视了该设计，并将其与当前的模型辅助方法进行了比较。我们使用为模拟和实施该设计而开发的新网络应用程序生成了 Conaway-Dunbar-Peddada 方法的操作特征，并将其与最近提出的基于毒性概率区间的 Keyboard 设计进行了比较。结果：在考虑的 20 个场景中的 17 个场景中，Conaway-Dunbar-Peddada 方法在剂量推荐的准确性和患者分配的安全性方面表现更好。在许多研究的场景中，Conaway-Dunbar-Peddada 方法将患者分配到最大耐受剂量以上的剂量的数量也少于 Keyboard 方法。总体而言，与近年来开发的模型辅助方法相比，Conaway-Dunbar-Peddada 方法的性能非常强，是一种可行的简单替代方法。结论：Conaway-Dunbar-Peddada 方法不依赖于整个剂量-毒性曲线的参数模型的规范和拟合来估计毒性概率，而其他基于模型的设计则依赖于类似的一组预试验规范来估计毒性概率。基于区间的方法，但与模型辅助方法不同，它能够在所有剂量水平上借用信息，从而提高其效率。我们希望对 Conaway-Dunbar-Peddada 方法的这种简洁研究以及用户友好型软件的可用性将增加其在实践中的使用。
