Precision medicine approach that assigns treatment according to an individual’s personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects’ profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.

中文翻译：

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通过半参数模型进行适应性临床试验的靶向设计

根据个人（包括分子）特征分配治疗的精准医学方法正在彻底改变医疗保健。用于临床试验设计的现有统计方法通常假设已知模型来估计治疗结果的特征，如果真实模型与假设模型相差甚远，则可能会产生有偏差的结果。本文旨在在 II 期多阶段自适应临床试验设计中实现模型稳健性。我们提出并研究了一种半参数回归混合模型，其中混合比例是根据受试者的概况指定的，并且每个子组分布仅假定为单峰分布以确保稳健性。回归参数和误差密度函数由半参数最大似然和等渗回归估计器估计。研究了估计的渐近性质。在实际数据分析后进行模拟研究以评估该方法的性能。