School of Statistics, East China Normal University, 3663 North Zhongshan Road, 200062, Shanghai, China.
Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, East China Normal University, Shanghai, China.
BMC Med Res Methodol. 2022 Oct 1;22(1):258. doi: 10.1186/s12874-022-01741-3.
Current dose-finding designs for phase I clinical trials can correctly select the MTD in a range of 30-80% depending on various conditions based on a sample of 30 subjects. However, there is still an unmet need for efficiency and cost saving.
We propose a novel dose-finding design based on Bayesian stochastic approximation. The design features utilization of dose level information through local adaptive modelling and free assumption of toxicity probabilities and hyper-parameters. It allows a flexible target toxicity rate and varying cohort size. And we extend it to accommodate historical information via prior effective sample size. We compare the proposed design to some commonly used methods in terms of accuracy and safety by simulation.
On average, our design can improve the percentage of correct selection to about 60% when the MTD resides at a early or middle position in the search domain and perform comparably to other competitive methods otherwise. A free online software package is provided to facilitate the application, where a simple decision tree for the design can be pre-printed beforehand.
The paper proposes a novel dose-finding design for phase I clinical trials. Applying the design to future cancer trials can greatly improve the efficiency, consequently save cost and shorten the development period.
目前的 I 期临床试验剂量发现设计可以根据 30 名受试者样本的各种条件，正确选择 30%至 80%范围内的最大耐受剂量。然而，仍然存在效率和成本节约方面的未满足需求。
我们提出了一种基于贝叶斯随机逼近的新的剂量发现设计。该设计的特点是通过局部自适应建模和毒性概率和超参数的自由假设来利用剂量水平信息。它允许灵活的目标毒性率和不同的队列大小。我们通过先验有效样本量将其扩展到可以容纳历史信息。我们通过模拟比较了该设计与一些常用方法在准确性和安全性方面的性能。
平均而言，当最大耐受剂量位于搜索域的早期或中期位置时，我们的设计可以将正确选择的百分比提高到约 60%，否则与其他竞争方法相比性能相当。提供了一个免费的在线软件包以方便应用，其中可以预先打印设计的简单决策树。
本文提出了一种用于 I 期临床试验的新的剂量发现设计。将该设计应用于未来的癌症试验中，可以大大提高效率，从而节省成本并缩短开发周期。
