Mozgunov Pavel, Gasparini Mauro, Jaki Thomas
Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Dipartimento di Scienze Matematiche (DISMA) Giuseppe Luigi Lagrange, Politecnico di Torino, Torino, Italy.
Stat Methods Med Res. 2020 Oct;29(10):3093-3109. doi: 10.1177/0962280220919450. Epub 2020 Apr 27.
In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.
在肿瘤学领域,联合治疗的应用越来越多。最近,针对联合治疗的I期剂量递增试验提出了几种剂量探索设计。大多数新设计使用预先指定的参数模型,将目标联合的搜索限制在特定形式的表面上。在这项工作中,我们提出了一种用于联合研究的新型无模型设计,该设计仅基于每个药物内部单调性的假设。具体而言,我们通过独立的贝塔分布对每个相邻联合之间的比率进行参数化。结果,该设计不需要指定任何特定的参数模型,也不需要关于毒性递增顺序的知识。我们将所提出的设计的性能与基于模型的部分排序连续重新评估方法以及另一种无模型替代方法——独立贝塔设计的乘积进行了比较。在一项广泛的模拟研究中,我们表明,所提出的设计在试验中导致正确选择目标联合的比例相当或更好,同时导致相同或更少的平均毒性反应数量。