使用无响应的基于人口的调查估计艾滋病毒: 一个部分识别方法
Oyelola A Adegboye1, Tomoki Fujii2, Denis Heng-Yan Leung2
1Menzies School of Health Research, Charles Darwin University, Casuarina, Australia.
Statistics in medicine
|May 17, 2024
概括
从调查数据中估计艾滋病毒的患病率是具有挑战性的,因为没有回应. 本研究引入了使用仪器变量强大的部分识别方法,提供了更具信息性和可靠的HIV估计,没有严格的假设.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 使用人口和健康调查 (DHS) 数据估计艾滋病毒患病率,因没有反应和拒绝测试而复杂化.
- 像归算这样的标准调整方法假设数据是随机丢失的,但通常情况并非如此.
- 仪表变量方法可以解释非随机缺失,但严重依赖仪器有效性.
研究的目的:
- 开发和评估一种可靠的艾滋病毒流行率估计方法,以解决调查数据中的非响应和检测拒绝问题.
- 提高基于人口调查的艾滋病毒流行率估计的信息性和可信度.
- 将拟议方法的性能与传统的归算和最坏情况的边界进行比较.
主要方法:
- 利用曼斯基的部分识别方法来构建HIV流行率的仪器变量边界.
- 使用了一组候选仪器,不要求所有仪器都是有效的.
- 通过模拟研究评估了该方法,并将其应用于赞比亚,马拉维和肯尼亚的DHS数据.
主要成果:
- 推算方法在轻微的非随机失踪的情况下产生了明显偏差的艾滋病毒流行率估计.
- 最坏情况的识别界限是强大的,但缺乏信息性.
- 拟议的工具变量界限联盟平衡了信息性和稳定性,即使有一些无效的工具.
结论:
- 部分识别边界为艾滋病毒流行率估计提供了一种可信和可靠的方法,没有严格的关于非响应机制的假设.
- 工具变量边界的结合提供了比最坏情况下的边界更有信息的替代方案,同时保持了稳定性.
- 这种方法对于使用受非响应影响的基于人口的调查数据进行准确的HIV监测至关重要.
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