Katz J, Zeger S L
Dana Center for Preventive Ophthalmology, Wilmer Institute, Johns Hopkins School of Medicine, Baltimore, MD.
Ann Epidemiol. 1994 Jul;4(4):295-301. doi: 10.1016/1047-2797(94)90085-x.
Cluster sampling can produce estimates of disease prevalence that are more variable than those from simple random sampling. This variance inflation or "design effect" depends on the prevalence of disease, the cluster sizes, and the magnitude of disease association within clusters. Design effects from prior surveys may not be appropriate for a planned survey if these components differ. We estimated within-cluster associations using pairwise odds ratios, which are more portable than design effects because they do not depend on the cluster sizes. Within-village pairwise odds ratios and design effects were estimated for fever and cough from four studies in Africa and Asia. Odds ratio ranged from 1.04 to 1.34 and 1.03 to 1.24, respectively. Design effects ranged from 2.35 to 6.80 for fever and 1.99 to 7.39 for cough. The design effect was more affected by cluster size and odds ratio than by variation in cluster size for a given sample size.
整群抽样所产生的疾病患病率估计值可能比简单随机抽样的估计值更具变异性。这种方差膨胀或“设计效应”取决于疾病的患病率、群集规模以及群集内疾病关联的程度。如果这些组成部分不同，先前调查的设计效应可能不适用于计划中的调查。我们使用成对比值比来估计群集内的关联，成对比值比比设计效应更具通用性，因为它们不依赖于群集规模。针对非洲和亚洲的四项研究，我们估计了村庄内发烧和咳嗽的成对比值比以及设计效应。发烧的比值比分别在1.04至1.34之间，咳嗽的比值比在1.03至1.24之间。发烧的设计效应在2.35至6.80之间，咳嗽的设计效应在1.99至7.39之间。对于给定的样本量，设计效应受群集规模和比值比的影响大于群集规模的变化。
