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Maximum-relevance weighted likelihood estimator: application to the continual reassessment method

机译:最大相关加权似然估计:在连续重估方法中的应用

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Typical phase I dose-finding clinical trials, notably in cancer, are characterized by a small number of patients (less than 40), a relatively high number of dose levels (4 to 6) and sequential dose allocation rules. In this setting, the Continual Reassessment Method (CRM) has been recommended as a dose allocation rule that provides a consistent method to converge to the maximal tolerated dose (MTD), possibly based on likelihood (CRML). In this adaptive design setting, we derived a Relevance Weighted Likelihood to propose a robust estimation of the MTD. The main idea is to weight the individual contributions to likelihood using a decreasing function of rank. We compare this method to the CRML throughout simulations.
机译:典型的I期剂量寻找临床试验(尤其是在癌症中)的特点是患者人数少(少于40名),剂量水平相对较高(4到6)和顺序剂量分配规则。在这种情况下,建议使用连续重新评估方法(CRM)作为剂量分配规则,该规则可能会基于可能性(CRML)提供一致的方法以收敛到最大耐受剂量(MTD)。在这种自适应设计设置中,我们推导了相关加权似然性,以提出对MTD的可靠估计。主要思想是使用秩的递减函数对可能性的个体贡献加权。在整个仿真过程中,我们将此方法与CRML进行了比较。

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