Designs for phase I trials of combinations of agents

药物组合的 I 期试验设计

• 批准号: 7987989
• 负责人: Mark R Conaway
• 金额: $ 29.64万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7987989
• 关键词: Accounting; Bayesian Method; Cancer Vaccines; Clinical Trials Design; Computer software; Cytotoxic agent; Data; Dimensions; Dose; Dose-Limiting; Goals; Internet; Language; Light; Maximum Tolerated Dose; Methods; Modeling; Outcome; Output; Patient Agents; Patients; Phase I Clinical Trials; Probability; Property; Site; Statistical Methods; Testing; Time; Toxic effect; Update; Work; anticancer research; base; design; efficacy testing; indexing; oncology; phase 1 study; preference; public health relevance; response; simulation

DESCRIPTION (provided by applicant): The majority of methods for the design of Phase I trials for in oncology are intended for studies involving a single cytotoxic agent. The goal of these studies is to estimate the 'maximally tolerated dose', the highest dose that can be administered with an acceptable level of toxicity. A key assumption of these methods is the monotonicity of the dose- response curve. In this case, the dose-response curve is said to follow a 'simple order' because the ordering of the probabilities of a 'dose-limiting toxicity' (DLT) for any pair of doses is known; administration of greater doses of the agent can be expected to produce DLT's in increasing proportions of patients. It is becoming increasingly common for combinations of agents to be tested in phase I trials. In these studies, the probabilities of a DLT associated with the dose combinations often follow a 'partial order' in that there are pairs of dose combinations for which the ordering of the probabilities is not known. This proposal uses Bayesian methods, combining features of the continual reassessment method and order restricted inference to develop designs for phase I trials in which the probabilities follow a partial order. In addition, we will adapt our methods for cycle-specific toxicities. Finally, we will develop internet-accessible software to assist users in designing and carrying out partially ordered phase I trials. Even though our emphasis is on phase I trials of combinations, the methods we develop can shed light on other issues in phase I trial design, including the study of ordered groups and trials of cancer vaccines. PUBLIC HEALTH RELEVANCE: Dose-finding trials of combinations of agents are becoming increasingly common in cancer research. While there are many proposed methods for single agent trials, there are relatively few options for designing phase I trials of combinations. As in single agent trials, it is crucial to find a combination of doses that can be administered with an acceptable level of toxicity in order that these new therapies can be tested for efficacy. Without adequate statistical methods potentially effective combinations may be discarded as too toxic or get tested in subsequent studies at sub-optimal dose combinations. The overall goal of this proposal is to develop designs for phase I trials of combinations of agents.

描述（由申请人提供）：肿瘤学 I 期试验的大多数设计方法旨在用于涉及单一细胞毒性药物的研究。这些研究的目的是估计“最大耐受剂量”，即在可接受的毒性水平下可以施用的最高剂量。这些方法的一个关键假设是剂量反应曲线的单调性。在这种情况下，剂量反应曲线被认为遵循“简单顺序”，因为任何剂量对的“剂量限制毒性”（DLT）概率的顺序是已知的；预计使用更大剂量的药物会在越来越多的患者中产生 DLT。在 I 期试验中测试药物组合变得越来越普遍。在这些研究中，与剂量组合相关的 DLT 的概率通常遵循“部分顺序”，因为存在一些剂量组合对，其概率的顺序是未知的。该提案使用贝叶斯方法，结合连续重新评估方法的特征和顺序限制推理来开发第一阶段试验的设计，其中概率遵循部分顺序。此外，我们将针对特定循环的毒性调整我们的方法。最后，我们将开发可通过互联网访问的软件，以帮助用户设计和进行部分订购的第一阶段试验。尽管我们的重点是组合的 I 期试验，但我们开发的方法可以阐明 I 期试验设计中的其他问题，包括有序组的研究和癌症疫苗的试验。 公共卫生相关性：药物组合的剂量探索试验在癌症研究中变得越来越普遍。虽然针对单药试验提出了许多方法，但设计组合的 I 期试验的选择相对较少。与单药试验一样，找到可以接受的毒性水平的剂量组合至关重要，以便测试这些新疗法的疗效。如果没有足够的统计方法，潜在有效的组合可能会因为毒性太大而被丢弃，或者在后续研究中以次优剂量组合进行测试。该提案的总体目标是开发药物组合第一阶段试验的设计。