STATISTICAL INFERENCE SPARSE ORDERED CATEGORICAL DATA

统计推断稀疏有序分类数据

• 批准号: 3302896
• 负责人: Alan Agresti
• 金额: $ 5.58万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-05-01 至 1994-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3302896
• 关键词: behavioral medicine; data collection methodology /evaluation; diagnosis service; information theory; mathematical model; mathematics; statistics /biometry

Many variables in health-related sciences are measured on ordered categorical scales. Contingency tables that display such data are often sparse, having few observations in many cells of the table. Two common reasons for this are (1) constraints of a study necessitate a small sample size, or (2) repeated measurement of responses for subjects at several occasions results in a multidimensional table with a large number of cells. The proposed research focuses on developing statistical methodology for these two situations. Exact methods will be developed for making inferences about the association between treatment and response, adjusting for relevant covariates. Small-sample analyses: Exact statistical tests for an ordered categorical response will be developed for the hypotheses of conditional independence and no three-factor interaction. Non-null inferences, such as exact confidence intervals for ordinal odd ratios, will also be developed. The methods will be connected to recently developed loglinear and logit models for ordinal data. Repeated categorical measurement analyses: Two types of analyses will be considered. One type models how marginal distributions of the response vary across occasions and according to values of covariates. The other type models the dependence among repeated responses. Special attention will be given to modeling inter-rater agreement, an important problem when several physicians make subjective evaluations on a categorical scale, using the same sample. For both types of analyses, semi-parametric methods will be developed to handle cases in which traditional maximum likelihood approaches are awkward or infeasible.

健康相关科学中的许多变量都是按顺序测量的。 分类尺度 显示此类数据的列联表通常 稀疏，在表格的许多单元格中几乎没有观察结果。 两种常见 其原因是：（1）研究的局限性需要小样本 大小，或（2）重复测量受试者在几个 会产生一个包含大量单元格的多维表。 拟议的研究重点是制定统计方法， 这两种情况。 将开发精确的方法来制造 关于治疗和反应之间关联的推断，调整 相关协变量。 小样本分析：有序分类的精确统计检验 将针对条件独立性的假设开发响应 没有三个因素的相互作用。 非空推理，例如精确推理 也将制定有序奇数比的置信区间。 的 方法将连接到最近开发的对数线性和logit模型 对于有序数据。 重复分类测量分析：将进行两种类型的分析。 考虑了 一种是模拟响应的边际分布 不同的场合和根据协变量的值而变化。 另 类型对重复响应之间的依赖性进行建模。 特别注意 将给予建模评分员之间的协议，一个重要的问题时， 几位医生在分类尺度上进行主观评价， 使用相同的样本。 对于这两种类型的分析，半参数方法 将被开发来处理传统的最大似然法 方法是笨拙的或不可行的。