STATISTICAL Inference for Sparse Categorical Data

描述：(申请人提供)：与健康相关的变量 科学通常是绝对的。显示此类数据的联想表如下 通常是稀疏的，在某些类别中几乎没有观察到的，因为(1)研究 可以有相对较少的对象，或(2)重复测量和其他形式 对答复进行聚类会为表格生成许多单元格。建议数 研究的重点是发展稀疏范畴的统计方法 数据。小样本统计方法在比较医疗保健方面很有用 样本量不足以使用标准大样本时的处理 方法：研究方法。集群数据的模型支持处理主题异构性和 重复测量的研究中的受试者内相关性 受试者，例如，在交叉研究中比较处理。 重复分类测量数据：将开发和评估方法 用于分析聚集的分类数据，重点放在 重复测量。一个共同的主题是性质的应用和研究。 包含随机效应的广义线性混合模型。特定主题 包括研究无分配和无分配对效率和偏差的影响 随机效应的参数方法，二进制和序数模型 对描述多中心等应用中的异构性的响应 临床试验，发展描述性和推论比较的方法 非嵌套模型，并在响应为 连续比例。 小样本分析：参数的小样本推断方法 将进一步发展和评估与分类数据的相关性。主题 包括改进的“准确的”置信度区间(即保证的置信度 至少是名义上的水平)比例的差异，赔率 比率和相对风险，以及改进的近似可信区间 这些措施。将比较两种类型的改进间隔(在某些情况下 在某些情况下，近似可能更好，因为 离散数据的精确方法)，并将为 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.