Exact Regression Software for Correlated Categorical Data

相关分类数据的精确回归软件

• 批准号: 8905963
• 负责人: PRALAY SENCHAUDHURI
• 金额: $ 9.96万
• 依托单位: CYTEL, INC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-05 至 2015-12-05
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8905963
• 关键词: Accounting; Adverse event; Algorithms; Area; Benchmarking; Biomedical Research; Characteristics; Complement; Computer Simulation; Computer software; Confidence Intervals; Data; Data Analyses; Databases; Development; Environmental Exposure; Family; Genetic; Goals; Individual; Logistic Regressions; Markov Chains; Measures; Methods; Modeling; Network-based; Normalcy; Ophthalmology; Outcome; Outcome Measure; Pattern; Performance; Phase; Procedures; Public Health; Publishing; Relative (related person); Reporting; Research; Research Personnel; Sample Size; Sampling; Software Tools; Source; Statistical Methods; System; Technology; Teratology; Testing; Time; United States National Institutes of Health; Work; analog; database of Genotypes and Phenotypes; improved; memory process; parallel processing; public health relevance; research and development; simulation; tool

 DESCRIPTION (provided by applicant): The overarching goal of the proposed research is to develop practical modeling tools - including exact regression procedures - for small or sparse samples of correlated categorical data. Such outcomes are common in biomedical research, especially in areas such as genetics, ophthalmology, and teratology. One can encounter correlated categorical data wherever multiple outcomes are measured on an individual over time, or on several different individuals who share common genetic or environmental exposures. A large body of methods has been developed for analyzing correlated categorical outcomes, which conventionally rely on large-sample distributional assumptions (e.g., approximate normality) to justify their inferences. When faced with a small or sparse sample of categorical data investigators have few viable analytic options, and none that allow for exact inferences with regard to estimation. Our proposed work will fill this gap, building on critical recent developments of both appropriate models and computational technology. During Phase I of this project, we will accomplish this by (1) developing an analogue to conditional logistic regression for correlated categorical data; (2) constructing an efficient network graphical algorithm for rapi computation of the exact distribution in Aim 1; and (3) Investigating the feasibility of incorporating these procedures into a SAS PROC. We plan to expand this work in Phase II by incorporating our new tools as a module in the LogXact software package; extending the exact regression procedure to accommodate Poisson and polychromous regression for correlated data; and significantly improving the computational efficiency of these new tools through efficient Monte Carlo sampling and parallel processing. We will also create a module for a SAS PROC, making these methods as widely available as possible to researchers and analysts.