用线性混合模型对全国微量营养素调查数据的分析:对未来调查的估计,预测和教训
Hakunawadi Alexander Pswarayi1, Edward J M Joy2, Dawd Gashu3
1School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, Loughborough, UK.
微量营养素调查可以使用线性混合模型 (LMM) 来准确估计人口,即使调查设计偏离. 这种方法有助于评估营养充足度,并有效规划未来的采样策略.
科学领域:
- 营养流行病学 营养流行病学
- 生物统计学 生物统计学
- 调查方法调查方法.
背景情况:
- 微量营养素缺乏会给公共卫生带来挑战,需要进行国家调查以监测人口状况.
- 基于设计的调查使用包含概率和样本权重来进行公正的估计.
- 线性混合模型 (LMM) 为基于模型的估计提供了替代方案,特别是当调查设计偏离时.
研究的目的:
- 为了证明LMM的实用性,从设计偏差的微量营养素调查中获得模型公正的估计.
- 使用LMM差异组件来评估替代采样设计和计划未来的调查.
- 用LMM评估埃塞俄比亚的区域微量营养素充足度.
主要方法:
- 将LMM应用于埃塞俄比亚国家微量营养素调查 (2016) 数据.
- 作为随机效应,纳入区域变化和嵌套结构 (家庭中的个人,人口普查区域内的家庭,区域内的EA).
- 估计的LMM参数,区域平均值 (蓝色),以及对采样/未采样区域的预测.
主要成果:
- 没有证据表明样本重量对血清度有信息.
- 获得的LMM标准误差和区域平均值的最佳线性公正估计 (BLUE).
- 评估了区域手段超过充足度值的概率,并为替代设计启动了差异.
结论:
- 当调查设计不被严格遵守时,LMM提供了一种可靠的方法来进行公正的估计和预测.
- 对于评估和优化未来的调查设计,LMM差异组件是有价值的.
- 这种方法提高了微量营养素状况评估的可靠性,并为公共卫生干预提供了信息.
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