Bayesian Adaptive Designs for Oncology Clinical Trials with Late-onset Outcomes

具有迟发结果的肿瘤学临床试验的贝叶斯自适应设计

• 批准号: 8116172
• 负责人: Ying Yuan
• 金额: $ 29.51万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8116172
• 关键词: Address; Aftercare; Algorithms; Cessation of life; Characteristics; Clinical; Clinical Trials; Clinical Trials Design; Computational algorithm; Data; Dose; Dose-Limiting; Enrollment; Investigation; Lead; Malignant Neoplasms; Markov Chains; Maximum Tolerated Dose; Methodology; Methods; Modeling; Outcome; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Phase I Clinical Trials; Phase I/II Trial; Phase II Clinical Trials; Physicians; Probability; Procedures; Property; Radiation therapy; Research; Research Personnel; Sample Size; Structure; Time; Toxic effect; Treatment Efficacy; Uncertainty; Weight; base; design; drug efficacy; improved; innovation; interest; oncology; phase 1 study; response; theories; tumor; user friendly software

DESCRIPTION (provided by applicant): The primary objectives of this proposal are to develop robust and efficient Bayesian adaptive designs for early phase oncology clinical trials with late-onset outcomes, and to propose a semi-parametric estimate of the dose-response curve. Conventional early phase trial designs typically assume that the toxicity and efficacy outcomes are observed shortly after the initiation of the treatment in order to assign an appropriate dose to patients newly enrolled in the trial. However, late-onset toxicity and efficacy are common in phase I studies. In the presence of late onset toxicity, using conventional trial designs may underestimate the toxicity probabilities, which would cause an undesirably large number of patients to be treated at overly toxic doses; and late onset efficacy often leads investigators to underestimate treatment efficacy and to incorrectly terminate a trial early. Moreover, parametric dose-toxicity and dose-efficacy model assumptions employed by many available early phase trial designs are not desirable, as asymptotic properties are generally not applicable for small sample sizes in early-phase trials. Misspecification of the dose-toxicity and dose-efficacy models may lead to poor operating characteristics of the trial. In this proposal, we develop robust and efficient Bayesian adaptive designs for phase I or phase I/II oncology clinical trials with late-onset outcomes. We formulate late-onset outcomes as a missing data problem and rigorously investigate characteristics and theories of the missing data induced by the late-onset outcomes. Based upon these investigations, we propose single- and multiple-agent phase I dose-finding trial designs, in which late-onset toxicity is addressed by the Bayesian data augmentation and the EM algorithm. To improve the robustness of the proposed trial designs, we propose to consider multiple dose-toxicity models simultaneously and then use Bayesian model averaging and model selection procedures to obtain robust estimates and desirable operating characteristics. Another common problem of interest in early-phase clinical trials is to estimate the relationship between the dose level of a drug and the probability of a response (e.g., toxicity or efficacy). We propose an efficient and robust semi-parametric approach that combines the advantages of parametric and nonparametric approaches. Our estimate of the dose-response curve is a weighted average of the parametric estimate and nonparametric estimate. When the true curve follows a parametric model assumption, the estimate converges to the parametric estimate, thus achieving high efficiency. When the parametric model does not hold, the estimate converges to the nonparametric estimate, thereby still providing a consistent estimate of the true dose response curve. PUBLIC HEALTH RELEVANCE: Cancer has been the second deaths-leading cause in U.S. The proposed research aims to provide more efficient, robust and innovative Bayesian cancer clinical trial designs to help physicians to develop new drugs and therapies to cure cancer.

描述（由申请人提供）：本提案的主要目标是为具有迟发结果的早期肿瘤学临床试验开发稳健且有效的贝叶斯自适应设计，并提出剂量反应曲线的半参数估计。传统的早期试验设计通常假设在治疗开始后不久观察到毒性和疗效结果，以便为新加入试验的患者分配适当的剂量。然而，迟发性毒性和疗效在 I 期研究中很常见。在存在迟发型毒性的情况下，使用传统的试验设计可能会低估毒性概率，这将导致大量患者接受过度毒性剂量的治疗；晚发疗效常常导致研究者低估治疗效果并错误地提前终止试验。此外，许多可用的早期试验设计所采用的参数剂量毒性和剂量功效模型假设是不可取的，因为渐近特性通常不适用于早期试验中的小样本量。剂量毒性和剂量功效模型的错误指定可能会导致试验的操作特性不佳。在本提案中，我们为具有迟发结果的 I 期或 I/II 期肿瘤临床试验开发了稳健且高效的贝叶斯自适应设计。我们将迟发结果表述为缺失数据问题，并严格研究迟发结果引起的缺失数据的特征和理论。基于这些研究，我们提出了单药和多药 I 期剂量探索试验设计，其中通过贝叶斯数据增强和 EM 算法解决迟发型毒性问题。为了提高所提出的试验设计的稳健性，我们建议同时考虑多个剂量毒性模型，然后使用贝叶斯模型平均和模型选择程序来获得稳健的估计和理想的操作特性。早期临床试验中另一个常见的问题是估计药物剂量水平与反应概率（例如毒性或功效）之间的关系。我们提出了一种有效且稳健的半参数方法，结合了参数方法和非参数方法的优点。我们对剂量反应曲线的估计是参数估计和非参数估计的加权平均值。当真实曲线遵循参数模型假设时，估计收敛到参数估计，从而实现高效率。当参数模型不成立时，估计值收敛到非参数估计值，从而仍然提供真实剂量响应曲线的一致估计值。 公共健康相关性：癌症已成为美国第二大死亡原因。拟议的研究旨在提供更高效、稳健和创新的贝叶斯癌症临床试验设计，以帮助医生开发治疗癌症的新药物和疗法。