STATISTICAL METHODOLOGY FOR CANCER CLINICAL TRIALS

说明(申请人摘要)：这项建议的长期目标是 确定I期临床的高效和合乎道德要求的设计 试验，为最大耐受量确定适当的估计程序 剂量(MTD)，并通过提供最先进的设计来影响实践 供调查人员使用的软件。 背景：I期临床试验通常规模很小，不受控制， 为确定最大耐受性而设计的人体受试者序贯研究 一种实验性药物的剂量。也许是因为I期临床试验 通常是非随机化的，不涉及大样本，也不 假设驱动的统计考量往往被忽视。然而， 没有准确找到正确的MTD的试验可能会导致不充分的 剂量水平(从有效性的角度来看)被传递到进一步 测试或剧毒剂量水平正在传递到后期试验。 “传统”设计已经流行了一段时间，病人在这种情况下 以三人为一组进行治疗，剂量的增加或降低取决于 在他们的回答上。这种方法简单地将MTD标识为 数据，因此估计是不相关的。其他人则采取了更正式的 方法，将MTD视为剂量-反应曲线的未知参数。 然后这个问题就变成了分位数估计问题。这就是我们的方法 接受这项建议。参数贝叶斯方法(例如，连续的 重新评估方法；使用过量控制的升级)和非参数 已经提出了方法(例如，随机行走规则)作为允许 对感兴趣的分位数的有效估计。 具体目的I导出分位数估计的贝叶斯最优设计 在分配的剂量水平不超过指定的 分位数。我们将这个问题推广到贝叶斯序贯设计，在 在具体目标中，同样的约束条件二.具体目标三扩展具体目标一 和11通过处理来自世卫组织毒性的非二进制有序响应 比例。我们建议使用比例赔率模型来推导约束条件 贝叶斯最优设计及其序贯模拟。具体目标四提出 使用随机游走规则来开发试验的非参数设计 序贯毒性分级表。我们会探讨适当的评估程序。 具体目标五.在具体目标六中，我们打算对 I期临床试验的现有方法学和已开发的方法学 在具体目标I-V中。我们打算使用最先进的计算技术 寻找利益的伦理参数的准确分布的设施。 最后，我们开发了界面友好的前端软件，方便了用户的操作 特定目的的I期临床试验。

DESCRIPTION (Applicant's abstract): The long-term goal of this proposal is to determine efficient and ethically attractive designs for phase I clinical trials, determine appropriate estimation procedures for the maximum tolerated dose (MTD), and to impact practice by providing state-of-the art design software for use by investigators. Background: Phase I clinical trials are typically very small, uncontrolled, sequential studies of human subjects designed to determine the maximum tolerate dose of an experimental drug. Perhaps because phase I clinical trials are generally non- randomized, do not involve large samples, and are not hypothesis-driven, statistical considerations have often been ignored. However, trials that do not accurately find the correct MTD may result in inadequate dose levels (from the standpoint oi effectiveness) being passed on to further testing or in highly toxic dose levels being passed on to later phase trials. "Conventional" designs have been popular for some time, where patients are treated in groups of three, and doses are escalated or de-escalated depending on their responses. Such methods simply identify an MTD as a function of the data, and hence estimation is not relevant. Others have taken a more formal approach, by treating the MTD as an unknown parameter of a dose-response curve. The problem then becomes one of quantile estimation. This is the approach we take in this proposal. Parametric Bayesian methods (e.g., continual reassessment method; escalation with overdose control) and nonparametric methods (e.g., random walk rules) have been proposed as designs that allow efficient estimation of a quantile of interest. Specific Aim I derives the Bayesian optimal design for estimation of a quantile under a constraint that the assigned dose levels do not exceed a specified quantile. We extend this problem into a Bayesian sequential design, under the same constraint, in specific Aim II. Specific Aim III extends Specific Aims I and 11 by dealing with non-binary ordinal responses from the WHO toxicity scale. We propose to use a proportional odds model to derive the constrained Bayesian optimal design and its sequential analog. Specific Aim IV proposes to use a random walk rule to develop a nonparametric design for a trial with ordinal toxicity scale. We will explore appropriate estimation procedures in Specific Aim V. In Specific Aim VI, we intend to do a formal comparison of existing methodology for phase I clinical trials with the methodology developed in Specific Aims I-V. We intend to use state-of-the-art computational facilities to find exact distributions of ethical parameters of interest. Finally, we develop user-friendly front-end software to facilitate the conduct of phase I clinical trials in Specific Aim VII.