Exact Inference Software for Correlated Categorical Data

用于相关分类数据的精确推理软件

• 批准号: 6736754
• 负责人: PRALAY SENCHAUDHURI
• 金额: $ 10.05万
• 依托单位: CYTEL, INC
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6736754
• 关键词: clinical research; computer human interaction; computer network; computer program /software; computer system design /evaluation; data management; information systems; method development; statistics /biometry

DESCRIPTION (provided by applicant): This is a Phase I SBIR proposal for the development of computer software performing small sample exact inference for correlated categorical data. Such data are now common in many, biomedical areas of research, such as genetics, ophthalmology, and teratology. In addition, exact methods are not available in any commercial package and are badly needed for accurate inference. By the end of Phase II we plan to develop tools for analyzing small as well as large data sets with correlated binary and correlated multivariate responses. This set of tools will compute point estimates and confidence intervals, as well as perform hypothesis tests, for several likelihood-based models for a multivariate binary response. In this Phase I effort, we will 1. build a stand-alone program with a simple user interface, including a data editor and a menu to specify fixed covariates and correlation structure of the data; 2. implement within this computer program a) an exact trend test for stratified ordered clustered binomial populations, b) an exact procedure to test for clustering c) an exact trend test for multiple outcome data In addition, we will investigate the feasibility of network-based Monte Carlo methods for fitting such models. Because there exists no commercial statistical software for such methods for exact inference, the resulting module will fill an important gap in statistical tools for categorical data analysis, and will be incorporated into new versions of the StatXact and LogXact, Cytel's flagship software packages, we shall also implement all these tools in widely used statistical package SAS as a SAS procedure.

描述（由申请人提供）：这是一项I期SBIR提案，用于开发对相关分类数据进行小样本精确推断的计算机软件。这些数据现在在许多生物医学研究领域很常见，如遗传学，眼科学和畸形学。此外，精确的方法在任何商业软件包中都不可用，并且非常需要精确的推理。在第二阶段结束时，我们计划开发工具，用于分析具有相关二元和相关多元响应的小型和大型数据集。这组工具将计算点估计值和置信区间，并为多变量二进制响应的几个基于似然的模型执行假设检验。在第一阶段的工作中，我们将1。建立一个具有简单用户界面的独立程序，包括一个数据编辑器和一个菜单，用于指定固定协变量和数据的相关结构; 2.在该计算机程序内实现 a）分层有序聚集二项总体的精确趋势检验， B）测试聚类的精确程序 c）多个结果数据的精确趋势检验此外，我们将研究基于网络的Monte Carlo方法拟合此类模型的可行性。由于目前还没有商业统计软件用于此类方法的精确推断，因此所产生的模块将填补分类数据分析统计工具的重要空白，并将纳入Cytel旗舰软件包StatXact和LogXact的新版本中，我们还将在广泛使用的统计软件包SAS中实现所有这些工具作为SAS程序。