发表机构
University of Granada（格拉纳达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种两步对数线性程序，用于交叉分类数据的图形表示与关联推断，解决疾病诊断中展开表示的退化解问题，并建立比值比检验与概况位置检验的等价性，应用于冠状动脉疾病诊断。
AI 中文摘要
生物计量科学，尤其是疾病诊断，通常关注交叉分类数据中关联的分析，其中距离关联模型为具有少量类别的非稀疏矩阵提供了图形解释。在此框架下，通常存在二元探索变量和响应变量，基于个体概况的分析具有重大意义。对于饱和模型，我们证明了在对数线性模型中，通常的线性关系在距离关联参数化下于全维度中得以保留。这使得一个两步程序得以实现，以促进在去除总体效应和主效应后以展开形式对关联进行分析和解释。所提出的程序能够处理由二元变量构成的概况的交叉分类数据，并且易于使用传统统计软件实现。对于疾病诊断，解决了展开表示中退化解的问题，以及确定概况位置之间显著差异的问题。考虑了一种基于比值比的独立性假设检验。此外，提出了一种程序来确定检验显著性的原因，避免了误差传播问题。在疾病诊断中，展示了比值比对等性检验与展开表示中两个概况位置对等性检验之间的等价性。结果已应用于一个关于冠状动脉疾病诊断的真实示例，将比值比与诊断测试的性能参数相关联。
英文摘要
Biometrical sciences and disease diagnosis in particular, are often concerned with the analysis of associations for cross-classified data, for which distance association models give us a graphical interpretation for non-sparse matrices with a low number of categories. In this framework, usually binary exploratory and response variables are present, with analysis based on individual profiles being of great interest. For saturated models, we show the usual linear relationship for log-linear models is preserved in full dimension for the distance association parameterization. This enables a two-step procedure to facilitate the analysis and the interpretation of associations in terms of unfolding after the overall and main effects are removed. The proposed procedure can deal with cross-classified data for profiles by binary variables, and it is easy to implement using traditional statistical software. For disease diagnosis, the problems of a degenerate solution in the unfolding representation, and that of determining significant differences between the profile locations are addressed. A hypothesis test of independence based on odds ratio is considered. Furthermore, a procedure is proposed to determine the causes of the significance of the test, avoiding the problem of error propagation. The equivalence between a test for equality of odds ratio pairs and the test for equality of location for two profiles in the unfolding representation in the disease diagnosis is shown. The results have been applied to a real example on the diagnosis of coronary disease, relating the odds ratios with performance parameters of the diagnostic test.
Comments20 pages, 6 tables, 1 figures
Journal refStatistics in Medicine, 2023, 42, 4897-4916
DOI：10.1002/sim.9854
