STATISTICAL Inference for Sparse Categorical Data

描述：（由申请人提供）：健康相关变量中测量的变量 科学往往是分类的。列联表显示了这些数据， 通常稀疏，在某些类别中观察很少，因为（1）研究 可能受试者相对较少，或（2）重复测量等形式 响应的聚类会为表格生成许多单元格。拟议 研究重点是稀疏分类统计方法的发展 数据小样本统计方法用于比较医疗 当样本量不足以使用标准大样本时的治疗 方法.聚类数据的模型能够处理受试者的异质性， 在重复测量的研究中， 例如，在交叉研究中比较治疗。 重复分类测量数据：将开发和评价方法 用于分析聚类的分类数据，重点关注具有 重复测量。一个共同的主题是应用和研究的性质， 包含随机效应的广义线性混合模型具体专题 包括研究对效率的影响和免费分配的偏见， 随机效应的参数方法，建模二进制和有序 描述应用程序中异质性的响应，例如多中心 临床试验，制定描述性和推断性比较的措施 的非嵌套模型，以及在响应为 连续比例。 小样本分析：参数的小样本推断方法 与分类数据的相关性将得到进一步发展和评价。主题 包括改进的“精确”置信区间（即 至少是名义水平）的比例差异，赔率 比率和相对风险，以及改进的近似置信区间， 这些措施。将比较两种类型的改进的间隔（在一些实施例中， 在某些情况下，近似值可能更好，因为 离散数据的精确方法），并将扩展 分层数据

DESCRIPTION: (provided by applicant): Variables measured in health-related sciences are often categorical. Contingency tables displaying such data are often sparse, having few observations in some categories, because (1) the study may have relatively few subjects, or (2) repeated measurement and other forms of clustering of responses produces many cells for the table. The proposed research focuses on development of statistical methods for sparse categorical data. Small-sample statistical methods are useful for comparing medical treatments when the sample size is insufficient to use standard large-sample methods. Models for clustered data enable handling of subject heterogeneity and consequent within-subject correlation in studies with repeated measurement of subjects, for instance in comparing treatments in cross-over studies. Repeated categorical measurement data: Methods will be developed and evaluated for analyzing clustered categorical data, focusing on applications with repeated measurement. A common theme is application and study of properties of generalized linear mixed models containing random effects. Specific topics include studying the effects on efficiency and bias of distribution-free and parametric approaches for the random effects, modeling binary and ordinal responses to describe heterogeneity in applications such as multi-center clinical trials, developing measures for descriptive and inferential comparison of non-nested models, and modeling clustered data when the response is a continuous proportion. Small-sample analyses: Small-sample inferential methods for parameters of relevance with categorical data will be further developed and evaluated. Topics include improved 'exact' confidence intervals (i.e. confidence level guaranteed to be at least the nominal level) for the difference of proportions, odds ratio, and relative risk, and improved approximate confidence intervals for these measures. The two types of improved intervals will be compared (In some cases, approximate may be better because of the inherent conservativeness of exact methods for discrete data), and extensions will be developed for stratified data.