MULTIVARIATE GROUP SEQUENTIAL CANCER CLINICAL TRIALS

描述：（改编自申请人的摘要）：出于伦理和经济考虑 原因，许多 III 期癌症临床试验都纳入了分组序贯试验 临时监测，以便在出现异常情况时可以适当提前停止 治疗效果明确。 通常，患者交错进入这些场所 研究是一种操作上的必要性，这可能会使分配变得复杂 使用的检验统计量的属性。 当这种困难加剧时 正在考虑多种结果，特别是如果其中一个或多个 结果是一次失败。 然而，许多重大进展 过去几十年的生存分析理论和实施 产生了丰富多样的统计数据，这些统计数据对许多人都很敏感 不同的替代假设：但灵活性和适用性 组顺序设置受到分析复杂性的严重限制 的基础分布。 拟议的研究将寻求解决 通过以下相互关联的目标来解决这些问题：（1）制定灵活的 用于准确确定零分布的蒙特卡罗方法 组序贯临床试验中的多变量统计检验 患者错开进入； (2) 开发多功能生存分析测试 程序具有更高的灵活性，可以推广到 允许绑定数据、分层以及两个以上的比较 治疗； (3) 使用以下方法评估和比较这些通用程序 分析、数据和模拟研究，以便明确最佳标准 可以确定使用； (4) 进一步扩展这些结果进行评估 分组序贯设计中的功效和样本量要求； (5) 使用 这些蒙特卡罗程序构建灵活的多元群 与临床假设相对应的连续边界 相关； (6) 开发合适的多变量评估方法 信息累积，以便设计的阿尔法支出方法 序贯临床试验可以应用于这种情况； (7) 实施 这些方法采用灵活、有据可查且用户友好的软件。 的 这项研究的主题是增加多种技术的适当利用 通过开发灵活的癌症临床试验终点数据 组序贯设置中的多变量检验统计量。

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.