STATISTICAL METHODS FOR COMMUNITY INTERVENTION TRIALS

描述(申请人摘要)：团体随机试验(GRTS)，其中 小组而不是个人被随机分配到治疗条件下， 以社区为基础的癌症预防研究的核心重要性，证据是 在过去十年中进行了大量的GRT。这个项目的总体目标是 建议为GRTS制定改进的统计评价方法。这个 根据这一建议开展的研究包括开发和评估 GRTS中的分析方法包括：1)基于随机化的方法 GRTS的推理功能更强大；2)试验结果的评估方法 无论匹配是否有效，匹配对GRT；以及3) 使广义线性混合模型更适合GRTS的方法。这个 该项目将利用数据，同时涉及理论和实证工作 来源和协作机会由大量 已完成并正在进行的研究。1)。基于随机化的理论工作 推理将使用加权置换测试并检查其属性，在 尤其是检测干预效果的统计能力。A加权 将开发基于排列的置信度区间，以考虑到个人 协变量调整。2)。配对分析的理论工作将 根据观察到的匹配项之间的相关性开发一个测试条件 连合。这种新方法的性能将与 传统的无条件方法既有解析的，也有模拟的。这样的一个 测试将允许进行GRTS的调查人员使用匹配或阻止 对可能与结果相关的因素进行控制，但又重新掌握了权力 在分析的时候如果匹配或阻挡是无效的。3)。 关于广义线性混合模型的理论工作将发展一种新的分析 适合于GRTS的方法，其中调查对象的数量 每个社区通常很大，而社区的数量通常很多 小的。与竞争程序相比，这种方法的偏倚和效率 将通过分析和模拟进行检验。

DESCRIPTION (Applicant's abstract): Group Randomized Trials (GRTs), in which groups rather than individuals are randomized into treatment conditions, are of central importance to community-based cancer prevention research, evidenced by the large number of GRTs conducted in the past decade. The overall goal of this proposal is to develop improved statistical evaluation methods for GRTS. The research carried out under this proposal consists of development and evaluation of analytical methods in GRTs including: 1) methods to make randomization-based inference more powerful for GRTS; 2) methods to evaluate trial results in the matched pair GRTs regardless of whether matching is effective or not; and 3) methods to make Generalized Linear Mixed Models more suitable for GRTS. The project will involve both theoretical and empirical work, drawing on data sources and collaborative opportunities provided by a large number of completed, and ongoing studies. 1). Theoretical work on randomzation-based inference will use a weighted permutation test and examine its properties, in particular the statistical power to detect an intervention effect. A weighted permutation-based confidence interval will be developed allowing for individual covariate adjustment. 2). Theoretical work on matched pair analysis will develop a test conditional on the observed correlation between matched conununities. The properties of this new method will be compared with traditional unconditional methods both analytically and via simulation. Such a test will allow investigators carrying out GRTs to use matching or blocking to control for factors potentially related to outcomes and yet recapture the power at the time of analysis if the matching or blocking is not effective. 3). Theoretical work on Generalized Linear Mixed Models will develop a new analysis method which is properly suited for GRTs where the number of survey subjects per community is usually large while the number of communities is usually small. The bias and efficiency of this method compared to competing procedures will be examined analytically and via simulation.