对贝叶斯式Hui-Walter和后勤回归隐性类模型的评估,以用模拟数据估计诊断测试特征
Haifang Ni1, Gerrit Koop2, Irene Klugkist3
1Department Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, 3508 TD Utrecht, the Netherlands; Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, 3508 TC Utrecht, the Netherlands.
Preventive veterinary medicine
|July 27, 2023
概括
在没有黄金标准的情况下,贝叶斯逻辑回归隐性类型的贝叶斯模型,结合群体集群,与传统的惠沃尔特模型相比,提供更准确的诊断测试准确度 (灵敏度和特异性) 估计.
科学领域:
- 流行病学 流行病学
- 兽医医学 兽医医学 兽医医学
- 生物统计学 生物统计学
背景情况:
- 在没有黄金标准的情况下估计诊断测试准确性在流行病学中至关重要.
- 贝叶斯的Hui-Walter (HW) 隐性类模型是常用的,但汇总数据,可能会丢失信息.
- 贝叶斯逻辑回归 (LR) 隐性类模型通过包括个人级别的共变量提供了一个替代方案.
研究的目的:
- 为了比较贝叶斯式HW和LR隐性类模型的性能,以估计测试灵敏度和特异性.
- 为了在模拟环境中评估这些模型,使用牛群中结核病的现实数据.
- 评估群体级聚类对估计准确性的影响.
主要方法:
- 模拟数据集具有预定义的真实疾病状态和测试属性.
- 结合了现实的人口流行率和对牛甲结核病的测试特征.
- 采用贝叶斯方法论,用于经典的HW和个人级别的LR隐性类模型,包括群体集群.
主要成果:
- LR模型始终提供后期估计更接近真实灵敏度和特异性值.
- 纳入群体级集群的LR模型显示了最高的准确性和更窄的估计间隔.
- 包括动物水平的风险因素并没有妨碍LR模型的性能.
结论:
- 个体级别贝叶斯逻辑回归隐性类模型比经典的HW模型更好,用于估计没有黄金标准的测试特征.
- 对等级结构的计算,例如群体集群,显著提高了这些估计的准确性.
- 这种方法提高了流行病学研究中的诊断准确性评估,特别是在兽医环境中.
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