Phase I clinical trials are essential to bringing novel therapies from chemical development to widespread use. Traditional approaches to dose-finding in Phase I trials, such as the '3+3' method and the Continual Reassessment Method (CRM), provide a principled approach for escalating across dose levels. However, these methods lack the ability to incorporate uncertainty regarding the dose-toxicity ordering as found in combination drug trials. Under this setting, dose-levels vary across multiple drugs simultaneously, leading to multiple possible dose-toxicity orderings. The Partial Ordering CRM (POCRM) extends to these settings by allowing for multiple dose-toxicity orderings. In this work, it is shown that the POCRM is vulnerable to 'estimation incoherency' whereby toxicity estimates shift in an illogical way, threatening patient safety and undermining clinician trust in dose-finding models. To this end, the Bayesian model averaged POCRM (BMA-POCRM) is proposed. BMA-POCRM uses Bayesian model averaging to take into account all possible orderings simultaneously, reducing the frequency of estimation incoherencies. The effectiveness of BMA-POCRM in drug combination settings is demonstrated through a specific instance of estimate incoherency of POCRM and simulation studies. The results highlight the improved safety, accuracy and reduced occurrence of estimate incoherency in trials applying the BMA-POCRM relative to the POCRM model.

翻译：I期临床试验对于将新型疗法从化学开发推广至广泛应用至关重要。传统I期试验剂量探索方法（如"3+3"法和持续重估法CRM）提供了跨剂量水平递升的原则性路径。然而，这些方法无法纳入组合药物试验中剂量-毒性排序的不确定性。在此情景下，剂量水平同时涉及多种药物变化，导致多种可能的剂量-毒性排序。偏序持续重估法（POCRM）通过允许存在多种剂量-毒性排序拓展至此类场景。研究表明，POCRM存在"估计不一致性"缺陷，即毒性估计以不合逻辑的方式偏移，危及患者安全并削弱临床医生对剂量探索模型的信任。为此，本文提出贝叶斯模型平均化POCRM（BMA-POCRM）。BMA-POCRM采用贝叶斯模型平均化同时考虑所有可能排序，从而降低估计不一致性发生频率。通过POCRM在特定实例中的估计不一致性现象及仿真研究，验证了BMA-POCRM在药物组合场景中的有效性。结果表明，相较于POCRM模型，采用BMA-POCRM的试验在安全性、准确性方面显著提升，且估计不一致性发生率降低。