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

描述(由申请人提供)：这项建议的主要目标是为早期肿瘤临床试验开发稳健和有效的贝叶斯适应性设计，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定在治疗开始后不久就观察毒性和疗效结果，以便为新参加试验的患者分配适当的剂量。然而，迟发性毒性和有效性在I期研究中很常见。在存在迟发性毒性的情况下，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；而迟发性疗效往往导致研究人员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采用的参数剂量-毒性和剂量-疗效模型假设是不可取的，因为渐近性质通常不适用于早期试验中的小样本量。剂量-毒性和剂量-疗效模型的错误说明可能会导致试验的操作特性较差。在这项建议中，我们为I期或I/II期肿瘤临床试验制定了稳健和有效的贝叶斯适应性设计，结果晚。我们将迟发结果描述为一个数据缺失问题，并严格研究了由迟发结果引起的数据缺失的特征和理论。在这些研究的基础上，我们提出了单因素和多因素的I期剂量发现试验设计，其中迟发毒性通过贝叶斯数据增强和EM算法来解决。为了提高试验设计的稳健性，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过程来获得稳健的估计和期望的操作特性。在早期临床试验中感兴趣的另一个常见问题是估计药物剂量水平与反应概率(例如，毒性或疗效)之间的关系。结合参数方法和非参数方法的优点，提出了一种高效、稳健的半参数方法。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。当真实曲线遵循参数模型假设时，估计收敛于参数估计，从而实现高效率。当参数模型不成立时，估计收敛于非参数估计，从而仍然提供对真实剂量响应曲线的一致估计。 公共卫生相关性：癌症已成为美国第二大致死原因。这项拟议的研究旨在提供更有效、更稳健和更具创新性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和疗法。

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.