POPULATION PREVALENCE ESTIMATES FROM COMPLEX SAMPLES
POPULATION PREVALENCE ESTIMATES FROM COMPLEX SAMPLES
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DOI：
10.1016/0895-4356(92)90040-t
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发表时间：
1992-04-01
影响因子：
7.2
通讯作者：
EVANS, DA
中科院分区：
文献类型：
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作者：
BECKETT, LA;SCHERR, PA;EVANS, DA
流行病学研究通常被设计用于几个目的。复杂的抽样计划是必要的，以确保足够的病例数量、病例和对照中相似的年龄分布。或比较研究的其他重要设计限制，可能会使估计人群或重要亚组中的流行率的额外目标很难用现有的计算机软件实现，该软件通常假定简单的随机样本选择。我们在这里考虑了从复杂样本中估计总体和亚组流行率的各种方法，包括粗略流行率、流行率的直接标准化以及使用Logistic回归进行标准化以平滑抽样组流行率。我们使用一个复杂的样本来说明这些方法，以估计阿尔茨海默病在城市社区中的患病率。文中还描述了在这种情况下各种模型下的仿真研究。我们的结论是，只要仔细检查Logistic模型的适合性，在标准化之前使用Logistic回归平滑抽样组患病率是估计总体患病率的有效方法。
Epidemiologic studies are often designed to serve several purposes. The complex sampling plans necessary to ensure an adequate number of cases, a similar age distribution among cases and controls. or other important design constraints for comparative studies may make the additional goal of estimating prevalence in the population or in important subgroups difficult to attain with existing computer software, which typically assumes simple random sample selection. We consider here various methods for estimating overall and subgroup prevalence from complex samples, including crude prevalences, direct standardization of prevalences, and standardization using logistic regression to smooth the sampling group prevalences. We illustrate these methods using a complex sample to estimate the prevalence of Alzheimer's disease in an urban community. A simulation study under various models in this setting is also described. We conclude that the use of logistic regression to smooth sampling group prevalences before standardization is an effective method for estimation of overall prevalence, provided that the adequacy of fit of a logistic model is carefully checked.