Department of Biostatistics, Indiana University, Indianapolis, IN, USA.
Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, IN, USA.
Stat Methods Med Res. 2021 Mar;30(3):892-903. doi: 10.1177/0962280220979328. Epub 2020 Dec 21.
The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity-efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. The CWL method makes no parametric model assumption on either the dose-response curve or the toxicity-efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.
延迟结局问题在早期剂量发现临床试验中很常见。由于毒性和疗效反应都受到延迟结局问题的影响,因此在 I/II 期临床试验中,这个问题变得更加棘手。由于缺乏对联合毒性-疗效分布进行建模的能力,现有的适用于 I 期试验的方法不能直接用于 I/II 期试验。在本文中,我们提出了一种条件加权似然(CWL)方法来解决这个问题。CWL 方法的关键思想是将联合概率分解为边际概率和条件概率的乘积,然后根据每个患者的实际随访时间对每个概率进行加权。CWL 方法对剂量-反应曲线或毒性-疗效相关性没有任何参数模型假设,因此可以应用于任何现有的 I/II 期试验设计。数值试验应用表明,所提出的 CWL 方法具有良好的操作特性。