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.

 描述（由申请人提供）：拟议研究的总体目标是为相关分类数据的小样本或稀疏样本开发实用的建模工具（包括精确的回归程序），这种结果在生物医学研究中很常见，尤其是在遗传学等领域。 、眼科和畸胎学。随着时间的推移，对一个人或几个具有共同遗传或环境暴露的不同个体进行多个结果的测量，人们可能会遇到相关的分类数据。已被开发用于分析相关的分类结果，通常依赖于大样本分布假设（例如正态性）来证明其推论，当面对小或稀疏的分类数据样本时，调查人员几乎没有可行的分析选项，而且没有一个近似的分析选项。我们提出的工作将填补这一空白，建立在适当模型和计算技术的关键最新发展的基础上，在该项目的第一阶段，我们将通过（1）开发模拟来实现这一目标。相关分类数据的条件逻辑回归；(2) 构建有效的网络图形算法，用于目标 1 中精确分布的 rapi 计算；(3) 研究将这些过程合并到 SAS PROC 中的可行性。通过将我们的新工具作为 LogXact 软件包中的模块进行第二阶段的工作；扩展精确回归过程以适应相关数据的泊松和多色回归，并通过高效的蒙特卡罗显着提高这些新工具的计算效率；我们还将为 SAS PROC 创建一个模块，使这些方法尽可能广泛地供研究人员和分析人员使用。