DEVELOPMENT OF STATISTICAL METHODS TO ANALYZE CLUSTER SAMPLES

本研究项目将研究统计分析的方法 来自簇样本的分类数据，其中 每个集群内的观测值可以是相关的，并且 可能会以不相等的概率选择观测值。在……里面 特别是，对以总体为基础的整群样本的分析 病例对照研究与横断面和纵向健康 对调查结果进行了分析。研究集中在开发 对Logistic回归和Mantel-Haenzel和Wolf的修正 可以解释复杂样本的霍尔丹程序 设计。使用计算机模拟来验证统计 在改进方法的开发中使用的近似。 这项研究的初步结果表明，修改后的 用于适当分析来自集群样本的数据的方法 考虑到集群内相关性结构和 观察值的权重不相等。这些方法将是 有助于分析婴儿喂养研究和重复怀孕 研究家庭构成集群的位置。

This research project will study statistical methods for analyzing categorical data that comes from cluster samples where the observations within each cluster may be correlated and where the observations may be selected with unequal probabilities. In particular, the analysis of cluster samples from population-based case-control studies and cross-sectional and longitudinal health surveys is examined. Research has concentrated on developing modifications to logistic regression and Mantel-Haenzel and Wolf- Haldane procedures that would account for the complex sample design. Computer simulations are used to validate statistical approximations used in the development of modified methods. Preliminary results from this research indicate that the modified methods for analyzing data from cluster samples appropriately take into account the intra-cluster correlation structure and the unequal weighting of the observations. These methods will be useful for analyzing infant feeding studies and repeat pregnancy studies where the family constitutes the cluster.