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: 7053934
• 负责人: PRALAY SENCHAUDHURI
• 金额: $ 39.52万
• 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 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7053934
• 关键词: clinical research; computer human interaction; computer program /software; computer system design /evaluation; human data; method development; statistics /biometry

DESCRIPTION (provided by applicant): This is a Phase II SBIR proposal for a major extension to Cytel's flagship software, StatXact, to perform small sample exact inference for correlated categorical data. Such data are common in biomedical research, especially in areas such as genetics, ophthalmology, and developmental toxicology. In this Phase II effort, we will develop correlated data analogues for most of the existing small sample procedures for independent data currently provided in the StatXact software. Specifically the resulting module will implement correlated data extensions of: 1) Exact tests of independence in unordered and ordered R x C contingency table. 2) Tests for differences in the distributions of 2 ordered multinomial populations. 3) Exact Mantel-Haenszel-type tests for assessing homogeneity of relative odds for stratified 2x2 tables, 4) Exact correlated data methods for situations in which independent factors vary across observations within a cluster. This extension will expand the applicability of such methods to a wider range of longitudinal and multiple outcome settings. Because implementing the above procedures can be computationally complex, a final goal of the proposal is the development of new algorithms to make these tools practical for general use. These include new efficient network-based algorithms and Monte Carlo simulation strategies for model fitting. The final product of this effort will be a toolbox of exact procedures will enable users to avoid relying on potentially poor large sample approximations when analyzing small sample correlated categorical data. This advantage will ultimately lead to more reliable analyses of such data. There is currently no software for such methods other than a limited prototype developed in Phase I of this proposal.

描述（由申请人提供）：这是Cytel旗舰软件StatXact的主要扩展的II期SBIR提案，用于对相关分类数据进行小样本精确推断。这些数据在生物医学研究中很常见，特别是在遗传学，眼科学和发育毒理学等领域。在第二阶段的工作中，我们将为StatXact软件中目前提供的独立数据的大多数现有小样本程序开发相关数据模拟。具体来说，所产生的模块将实现相关的数据扩展：1）在无序和有序的R × C列联表的独立性的精确测试。2)2个有序多项总体分布的差异检验。3)精确Mantel-Haenszel型检验，用于评估分层2x2表的相对优势的同质性，4）精确相关数据方法，用于独立因素在聚类内的观察结果之间变化的情况。这一扩展将扩大这种方法的适用性，以更广泛的纵向和多个结果的设置。由于实现上述过程可能在计算上很复杂，因此该提案的最终目标是开发新算法，以使这些工具实用于一般用途。其中包括新的有效的基于网络的算法和蒙特卡罗模拟策略的模型拟合。这一努力的最终产品将是一个工具箱的确切程序将使用户能够避免依赖于潜在的穷人大样本近似时，分析小样本相关的分类数据。这一优势将最终导致对此类数据进行更可靠的分析。目前，除了在本提案第一阶段开发的有限原型之外，还没有用于这种方法的软件。