MULTIVARIATE GROUP SEQUENTIAL CANCER CLINICAL TRIALS

描述：(改编自申请者摘要)：伦理和经济 原因是，许多三期癌症临床试验纳入了分组序贯 临时监测允许在出现 治疗效果明显。通常，患者交错进入这些 学习是一种操作需要，它会使分配复杂化。 使用的测试统计信息的属性。当出现以下情况时，这种困难就会加剧 正在考虑多个结果，特别是如果一个或多个 结果是一段失败的时光。然而，在以下方面取得了一些重大进展 生存分析理论及其应用几十年 产生了丰富多样的统计数据，这些数据可能对许多人很敏感 不同的替代假设：但灵活性和适用性 群体顺序设置受到分析复杂性的严重限制 基本分布的信息。拟议的研究将寻求解决 这些问题通过以下相互关联的目标：(1)制定灵活的 精确确定零点分布的蒙特卡罗方法 一组序贯临床试验中的多变量统计检验 患者交错进入；(2)开发通用生存分析测试 具有改进的灵活性的过程，可概括为 允许绑定数据、分层和两个以上的比较 治疗；(3)评估和比较这些通用的程序使用 分析、数据和模拟研究，以便明确最佳标准 可以建立用途；(4)进一步扩展这些结果以进行评估 成组序贯设计中的功率和样本量要求；(5)使用 构造灵活多变量群的蒙特卡罗方法 与临床假说相对应的顺序边界 相关；(6)制定合适的多变量评估方法 信息应计，所以阿尔法支出法设计 可将序贯临床试验应用于此设置；以及(7)实施 这些方法包含在灵活、文档齐全且用户友好的软件中。这个 这项研究的主题是增加对多个 通过灵活开发癌症临床试验的终点数据 分组序贯设置中的多变量检验统计。

DESCRIPTION: (Adapted from applicant's abstract): For ethical and economic reasons, many Phase III cancer clinical trials incorporate group sequential interim monitoring to permit appropriate early stopping in the presence of a clear treatment effect. Frequently, staggered entry of patients into these studies is an operational necessity which can complicate the distributional properties of the test statistics used. This difficulty is exacerbated when multiple outcome are being considered, especially if one or more of the outcomes is a failure time. However, a number of significant advances in survival analysis theory and implementation over the last several decades have resulted in a rich variety of statistics which can be sensitive to many different alternative hypotheses: but the flexibility and applicability to the group sequential setting is severely limited by the analytic complexity of the underlying distributions. The proposed research will seek to address these issues through the following interrelated goals: (1) Develop flexible Monte Carlo methods for accurately determining the null distribution of multivariate statistical tests in a group sequential clinical trial with staggered entry of patients; (2) Develop versatile survival analysis test procedures possessing improved flexibility, which can be generalized to permit tied data, stratification, and the comparison of more than two treatments; (3) Evaluate and compare these versatile procedures using analytic, data, and simulation studies so that clear criteria for optimal use can be established; (4) Further extend these results for evaluating power and sample size requirements in group sequential designs; (5) Use these Monte Carlo procedures to construct flexible multivariate group sequential boundaries which correspond to hypotheses which are clinically relevant; (6) Develop a suitable method of assessing multivariate information accrual so that the alpha-spending approach for designing sequential clinical trials can be applied to this setting; and (7) Implement these methods in flexible, well documented, and user-friendly software. The theme for this research is increasing appropriate utilization of multiple endpoint data in cancer clinical trials through development of flexible multivariate test statistics in a group sequential setting.