嵌套组测试算法的高效设计,用于在集群数据中识别疾病.
Ana F Best1, Yaakov Malinovsky2, Paul S Albert3
1Biostatistics Branch, Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Journal of applied statistics
|July 12, 2023
概括
组测试可以降低疾病查成本. 对集群数据的计算,如地理艾滋病毒流行率,提高了与集群之间显著差异的更高流行病的效率.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- 群体检测方法在历史上降低了罕见疾病查成本.
- 有限的研究存在于优化群体测试集群疾病流行数据,如艾滋病毒的地理变化.
- 有效的组测试设计对于具有成本效益的疾病监测和查至关重要.
研究的目的:
- 评估组测试设计,其中包括在聚类人群中估计疾病流行率.
- 根据估计的患病率来确定最佳的群体规模和测试策略,以尽量减少每个受试者的平均测试.
- 为了比较不同方法来估计患病率 (集群特定与常见) 和组建策略.
主要方法:
- 使用每个集群内的固定大小子集的个体测试来估计患病率.
- 根据估计的患病率,应用群体测试算法来选择最佳群体大小.
- 对设计进行比较,考虑集群特异性与常见流行,不同组测试算法,集群内部和集群间的分组以及错误分类.
- 使用艾滋病毒携带者识别和抗癌化合物查的实践应用的评估.
主要成果:
- 对于低患病率的疾病,在组测试设计中考虑聚类并没有显著的优势.
- 对于患病率较高和集群间异质性较高的疾病,将集群纳入研究设计显著提高了效率.
- 选择流行率估计 (集群特定或共同) 和组建影响整体效率.
结论:
- 组测试设计应考虑到人口聚类在处理更高流行率和显著异质性的疾病时.
- 该研究提供了实用建议,以优化群组测试策略在集群人口中,通过现实世界的例子证明了这一点.
- 根据疾病流行率和人口结构量身定制群体测试方法,提高了查效率和成本效益.
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