DEVELOPMENT OF STATISTICAL METHODS TO ANALYZE CLUSTER SAMPLES

本研究项目将研究统计分析的方法 来自集群样本的分类数据，其中观察值 在每个集群内可以是相关的，并且观测可以是 以不相等的概率被选中。尤其是，分析了 从以人群为基础的病例对照研究和 对横断面和纵向健康调查进行了审查。研究表明， 专注于开发对Logistic回归的修改和 Mantel-Haenzel和Wolf-Haldane程序将解释 复杂的样本设计。使用计算机模拟来验证 在改进方法的发展中使用的统计近似。 本研究的初步结果表明，改进后的方法 为了分析来自集群样本的数据，适当地考虑 簇内相关性结构和不等权重 观察。这些方法将有助于分析婴儿喂养。 在家庭构成的情况下进行研究和重复怀孕研究 集群。

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.