Statistics and Decision Sciences, Janssen Research & Development, LLC, Raritan, NJ, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA.
J Biopharm Stat. 2020 Nov 1;30(6):964-978. doi: 10.1080/10543406.2020.1818251. Epub 2020 Sep 14.
Many Phase I trial designs have been developed to improve upon the standard design. These designs can be classified as long-memory designs, for example, the continual reassessment method (CRM), and short-memory designs such as the modified toxicity probability interval (mTPI) design. Long-term memory designs use all data but their performance can be negatively affected by the model misspecification. Short-term memory designs only use data at the current dose and might lose efficiency as a result. To overcome these issues, we propose a regularized CRM (rCRM). The rCRM offers a trade-off between long-term memory and short-term memory methods. The rCRM gives more weight to data obtained at the doses with the estimated probability of toxicity closer to the target toxicity rate. The addition of a regularization term has an effect of shrinking the dimension of the model and leads to improved performance of the 2-parameter CRM. The rCRM is a good design choice to guide assignments in an expansion cohort phase of a dose-finding trial since dose assignments do not seem to change as often as in corresponding CRMs.
许多 I 期临床试验设计已经被开发出来以改进标准设计。这些设计可以分为长记忆设计，例如，连续再评估方法（CRM），和短记忆设计，如改良毒性概率区间（mTPI）设计。长记忆设计使用所有数据，但它们的性能可能会受到模型误设定的负面影响。短期记忆设计只使用当前剂量的数据，因此可能会失去效率。为了克服这些问题，我们提出了一种正则化 CRM（rCRM）。rCRM 在长期记忆和短期记忆方法之间提供了一种权衡。rCRM 更重视在毒性估计概率接近目标毒性率的剂量下获得的数据。添加正则化项的效果是缩小模型的维度，从而提高了 2 参数 CRM 的性能。rCRM 是指导剂量发现试验扩展队列阶段分配的一个不错的设计选择，因为剂量分配似乎不像相应的 CRM 那样经常改变。
