Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials
Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials
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DOI：
10.1214/09-sts315
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发表时间：
2010-05-01
影响因子：
5.7
通讯作者：
Thall, Peter F.
中科院分区：
文献类型：
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作者：
Thall, Peter F.
早期临床试验是评估潜在的新抗病药物或药物组合在人体中的作用的第一步。通常被称为“I期”或“I/II期”试验，这些实验通常具有确定可接受剂量的名义科学目标，最常见的是基于不良事件概率。这源于I期试验的传统，以评估用于治疗癌症的细胞毒性药物，尽管某些方法可以应用于其他医疗环境，例如治疗中风或免疫性疾病。早期试验的大多数现代统计学设计包括基于模型的结果适应性决策规则，这些规则根据试验中先前患者的数据为连续患者队列选择剂量。然而，由于其复杂性，需要密集的基于计算机的数据监测以及医学界对变化的抵制，这种设计在临床实践中的使用有限。尽管如此，许多基于模型的结果自适应设计的实际应用在患者受益和科学结果方面都非常成功。在本文中，我将回顾几个贝叶斯早期试验设计，这些设计是为适应特定临床环境中治疗方案和患者结局的特定复杂性而定制的。
An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.