一种两步的日志线性程序,用于图形表示和推断疾病诊断的交叉分类数据中的关联
J Fernando Vera1, José A Roldán-Nofuentes1
1Department of Statistics and O.R., University of Granada, Granada, Spain.
Statistics in medicine
|August 25, 2023
概括
距离关联模型为分析生物识别和疾病诊断中的交叉分类数据提供图形解释. 本研究介绍了分析个人资料的两步程序,并解决了疾病诊断展开表征的挑战.
科学领域:
- 生物识别科学 生物识别科学
- 疾病诊断 疾病诊断
- 统计建模 统计建模
背景情况:
- 对交叉分类数据中的关联分析对于生物识别和疾病诊断至关重要.
- 距离关联模型为具有很少类别的非散列矩阵提供图形解释.
- 个人资料分析具有显著的兴趣,通常涉及二元探索和响应变量.
研究的目的:
- 使用距离关联参数化,在全维的日志线性模型中保持线性关系.
- 引入一个两步程序,以分析和解释协会的展开.
- 解决疾病诊断方面的问题,包括退化解决方案和确定显著的概况差异.
主要方法:
- 使用和模型来证明在距离关联参数化中的线性关系的保存.
- 开发了一种两步程序,用于分析与二进制配置文件的交叉分类数据.
- 提出了基于赔率比率的独立性假设测试和确定重要原因的程序.
主要成果:
- 拟议的程序通过消除总体和主要影响,促进了协会的分析和解释.
- 该方法有效地处理使用二进制变量的配置文件的交叉分类数据,并且可以在标准软件中实现.
- 在疾病诊断的展开代表中,测试几率比率对的平等性和配置位置的平等性之间显示了等价性.
结论:
- 开发的两步程序简化了用于疾病诊断的交叉分类数据中的关联分析.
- 该研究涉及并提供了在展开的表示中退化的解决方案和意义测试的解决方案.
- 通过使用冠状动脉疾病诊断的真实实例来验证这些发现,将几率比率与诊断测试性能联系起来.
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