STATISTICAL METHODS FOR COMMUNITY INTERVENTION TRIALS

描述（申请人摘要）：分组随机试验（GRT），其中 组而不是个体被随机分配到治疗条件中， 社区癌症预防研究的核心重要性， 在过去十年中进行了大量的GRT。这个项目的总体目标是 建议开发改进的GRTS统计评估方法。的 根据该建议进行的研究包括开发和评估 GRT中的分析方法包括：1）使基于随机化的方法 推理更强大的GRTS; 2）方法，以评估试验结果， 匹配对GRT，而不管匹配是否有效;以及3） 方法，使广义线性混合模型更适合GRTS。的 该项目将涉及理论和实证工作，利用数据 大量的资源和合作机会， 已完成和正在进行的研究。1）。基于随机化的理论工作 推理将使用加权排列测试并检查其属性， 特别是检测干预效果的统计功效。加权 将开发基于置换的置信区间，允许个体 协变量调整2）。匹配对分析的理论工作将 以观察到的匹配之间的相关性为条件， conununities.这种新方法的性能将与 传统的无条件方法，无论是分析和通过模拟。这样的 测试将允许进行GRT的研究人员使用匹配或阻断， 控制可能与结果相关的因素， 在分析时，如果匹配或阻塞无效。3）。 广义线性混合模型的理论工作将发展一种新的分析 一种适合GRT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 小了与竞争程序相比，该方法的偏倚和效率 将通过分析和模拟进行检查。

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.