Fan Shenghua, Lee Bee Leng, Lu Ying
Department of Statistics and Biostatistics, California State University, East Bay, Hayward, 94542, CA, USA.
Department of Mathematics and Statistics, San Jose State University, San Jose, 95192, CA, USA.
Stat Biosci. 2020 Jul;12(2):146-166. doi: 10.1007/s12561-020-09272-5. Epub 2020 Mar 26.
A curve-free, Bayesian decision-theoretic two-stage design is proposed to select biological efficacious doses (BEDs) for phase Ia/Ib trials in which both toxicity and efficacy signals are observed. No parametric models are assumed to govern the dose-toxicity, dose-efficacy, and toxicity-efficacy relationships. We assume that the dose-toxicity curve is monotonic non-decreasing and the dose-efficacy curve is unimodal. In the phase Ia stage, a Bayesian model on the toxicity rates is used to locate the maximum tolerated dose. In the phase Ib stage, we model the dose-efficacy curve using a step function while continuing to monitor the toxicity rates. Furthermore, a measure of the goodness of fit of a candidate step function is proposed, and the interval of BEDs associated with the best fitting step function is recommended. At the end of phase Ib, if some doses are recommended as BEDs, a cohort of confirmation is recruited and assigned at these doses to improve the precision of estimates at these doses. Extensive simulation studies show that the proposed design has desirable operating characteristics across different shapes of the underlying true toxicity and efficacy curves.
本文提出了一种无曲线的贝叶斯决策理论两阶段设计方法，用于在同时观察到毒性和疗效信号的Ia/Ib期试验中选择生物有效剂量（BED）。我们不假设参数模型来描述剂量-毒性、剂量-疗效和毒性-疗效关系。我们假设剂量-毒性曲线是单调非递减的，剂量-疗效曲线是单峰的。在Ia期，使用关于毒性率的贝叶斯模型来确定最大耐受剂量。在Ib期，我们使用阶跃函数对剂量-疗效曲线进行建模，同时继续监测毒性率。此外，还提出了一种衡量候选阶跃函数拟合优度的方法，并推荐了与最佳拟合阶跃函数相关的BED区间。在Ib期结束时，如果某些剂量被推荐为BED，则招募一组确认队列并将其分配到这些剂量，以提高这些剂量估计的精度。大量的模拟研究表明，所提出的设计在不同形状的潜在真实毒性和疗效曲线上具有理想的操作特性。
