This paper presents the Burdened Bayesian Logistic Regression Model (BBLRM), an enhancement to the Bayesian Logistic Regression Model (BLRM) for dose-finding in phase I oncology trials. Traditionally, the BLRM determines the maximum tolerated dose (MTD) based on dose-limiting toxicities (DLTs). However, clinicians often perceive model-based designs like BLRM as complex and less conservative than rule-based designs, such as the widely used 3+3 method. To address these concerns, the BBLRM incorporates non-DLT adverse events (nDLTAEs) into the model. These events, although not severe enough to qualify as DLTs, provide additional information suggesting that higher doses might result in DLTs. In the BBLRM, an additional parameter $\delta$ is introduced to account for nDLTAEs. This parameter adjusts the toxicity probability estimates, making the model more conservative in dose escalation. The $\delta$ parameter is derived from the proportion of patients experiencing nDLTAEs within each cohort and is tuned to balance the model's conservatism. This approach aims to reduce the likelihood of assigning toxic doses as MTD while involving clinicians more directly in the decision-making process. The paper includes a simulation study comparing BBLRM with the traditional BLRM across various scenarios. The simulations demonstrate that BBLRM significantly reduces the selection of toxic doses as MTD without compromising, and sometimes even increasing, the accuracy of MTD identification. These results suggest that integrating nDLTAEs into the dose-finding process can enhance the safety and acceptance of model-based designs in phase I oncology trials.

翻译：本文提出负担贝叶斯逻辑回归模型（BBLRM），作为贝叶斯逻辑回归模型（BLRM）在I期肿瘤试验剂量探索中的增强版本。传统上，BLRM基于剂量限制性毒性（DLT）确定最大耐受剂量（MTD）。然而，临床医生通常认为基于模型的设计（如BLRM）比基于规则的设计（如广泛使用的3+3方法）更复杂且保守性不足。为解决这些问题，BBLRM将非剂量限制性不良事件（nDLTAE）纳入模型。这些事件虽未严重到构成DLT，但能提供额外信息提示更高剂量可能导致DLT。BBLRM引入额外参数$\delta$以处理nDLTAE，该参数通过调整毒性概率估计使模型在剂量递增时更为保守。$\delta$参数根据各队列中经历nDLTAE的患者比例推导，并通过调谐以平衡模型保守性。该方法旨在降低将毒性剂量指定为MTD的可能性，同时让临床医生更直接地参与决策过程。本文通过模拟研究比较BBLRM与传统BLRM在不同场景下的表现。模拟结果表明，BBLRM在不影响（有时甚至提高）MTD识别准确性的前提下，显著降低了将毒性剂量选为MTD的概率。这些结果提示，将nDLTAE整合至剂量探索过程可提升I期肿瘤试验中基于模型设计的安全性与可接受性。