Dose-finding trials for oncology studies are traditionally designed to assess safety in the early stages of drug development. With the rise of molecularly targeted therapies and immuno-oncology compounds, biomarker-driven approaches have gained significant importance. In this paper, we propose a novel approach that incorporates multiple values of a predictive biomarker to assist in evaluating binary toxicity outcomes using the factorization of a joint model in phase I dose-finding oncology trials. The proposed joint model framework, which utilizes additional repeated biomarker values as an early predictive marker for potential toxicity, is compared to the likelihood-based continual reassessment method (CRM) using only binary toxicity data, across various dose-toxicity relationship scenarios. Our findings highlight a critical limitation of likelihood-based approaches in early-phase dose-finding studies with small sample sizes: estimation challenges that have been previously overlooked in the phase I dose-escalation setting. We explore potential remedies to address these challenges and emphasize the appropriate use of likelihood-based methods. Simulation results demonstrate that the proposed joint model framework, by integrating biomarker information, can alleviate estimation problems in the the likelihood-based continual reassessment method (CRM) and improve the proportion of correct selection. However, we highlight that the inherent data limitations in early-phase dose-finding studies remain a significant challenge that cannot fully be overcomed in the frequentist framework.

翻译：肿瘤学研究的剂量递增试验传统上旨在评估药物开发早期的安全性。随着分子靶向疗法和免疫肿瘤学化合物的兴起，生物标志物驱动的方法已变得至关重要。本文提出了一种新颖方法，通过联合模型的因子分解，在肿瘤学I期剂量递增试验中纳入预测性生物标志物的多个测量值，以辅助评估二元毒性结局。所提出的联合模型框架利用额外的重复生物标志物测量值作为潜在毒性的早期预测指标，与仅使用二元毒性数据的基于似然的持续再评估方法（CRM）进行了比较，涵盖了多种剂量-毒性关系场景。我们的研究结果凸显了基于似然的方法在小样本早期剂量递增研究中的一个关键局限：在I期剂量递增环境中先前被忽视的估计难题。我们探讨了应对这些挑战的潜在解决方案，并强调了基于似然方法的合理使用。模拟结果表明，所提出的联合模型框架通过整合生物标志物信息，能够缓解基于似然的持续再评估方法（CRM）中的估计问题，并提高正确选择的比例。然而，我们强调早期剂量递增研究中固有的数据限制仍然是一个重大挑战，在频率学框架中无法完全克服。