Decision Rules for Multiple Endpoints in Clinical Trials

临床试验中多个终点的决策规则

• 批准号: 7281684
• 负责人: AJIT C TAMHANE
• 金额: $ 17.78万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7281684
• 关键词: Case Study; Classification; Clinical Trials; Complex; Computer software; Confusion; Data; Drug Evaluation; Economic Inflation; End Point; Evaluation; Facility Construction Funding Category; Free Will; Gatekeeping; Goals; Health Benefit; Methodology; Methods; Modification; Outcome; Pharmaceutical Preparations; Pharmacologic Substance; Procedures; Property; Protocols documentation; Public Health; Research; SAS; Specific qualifier value; Statistical Methods; Testing; Trees; Work; Writing; base; desire; prevent; statistics; theories; trial comparing

DESCRIPTION (provided by applicant): Many clinical trials compare a new treatment with a control on multiple endpoints. Protocols often specify complex decision rules that involve a combination of superiority and equivalence (non-inferiority) tests on primary, co-primary and secondary endpoints; some tests may be conditional on the outcomes of the other tests. Also, it is not uncommon to find sponsors proposing an adaptive modification of the protocol rule. Statistical properties of such rules are generally unknown and often they do not control the desired alpha level. This leads to much debate and confusion in the statistical evaluation of drug trials data. The long-term goal of the proposed research is to provide a general systematic framework for analyzing such rules and determining optimal allocation of the desired alpha level among the component tests to maximize power. Two approaches will be explored in achieving the research goals, both related to the theory of multiple comparisons. The first approach uses union-intersection and intersection-union tests, and assumes multivariate normality. The second approach uses the theory of gatekeeping procedures, and generalizes parallel and serial gatekeeping methods to what we call as multiple tree gatekeeping procedures. This approach is based on p-values of component test statistics and does not assume any particular distribution for the data. Bootstrap methodology will be used to implement the tests in many situations because of unknown correlations among the endpoints or non-normality of the data. The primary public health benefit of the proposed research is that it will facilitate correct statistical evaluation of multiple endpoints data. In particular, it will prevent approval of an inefficacious drug or a treatment which may be shown to be effective if incorrect statistical methods are used that do not properly control the type I error inflation caused by multiple tests on the endpoints.

描述（由申请人提供）：许多临床试验将新治疗方法与对多个终点的控制进行了比较。协议通常指定复杂的决策规则，该规则涉及对主要，共同主要和次要终点的优势和等效性（非效率）测试的结合；某些测试可能是基于其他测试结果的条件。同样，找到提出对协议规则的自适应修改的赞助商并不少见。此类规则的统计特性通常是未知的，通常不控制所需的alpha水平。这在药物试验数据的统计评估中导致了很多争论和混乱。拟议研究的长期目标是提供一个一般的系统框架，用于分析此类规则并确定组件测试中所需的α级别的最佳分配以最大程度地提高功率。在实现研究目标时将探索两种方法，这两种方法都与多个比较理论有关。第一种方法使用工会交流和交叉点测试，并假定多元正态性。第二种方法使用守门过程的理论，并将平行和串行的守门方法推广到我们称为多个树守护过程的方法。此方法基于组件测试统计数据的p值，并且不假定数据的任何特定分布。 Bootstrap方法将在许多情况下用于在许多情况下实施测试，因为端点之间的相关性未知或数据的非正态性之间的相关性。拟议的研究的主要公共卫生好处是，它将促进对多个终点数据的正确统计评估。特别是，如果使用不正确控制端点上多次测试引起的I型误差通货膨胀的统计方法，它将防止批准效率低下的药物或治疗方法。