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来自初级保健研究的聚类内相关性模式,以指导研究设计和分析。

Patterns of intra-cluster correlation from primary care research to inform study design and analysis.

作者信息

Adams Geoffrey, Gulliford Martin C, Ukoumunne Obioha C, Eldridge Sandra, Chinn Susan, Campbell Michael J

机构信息

Department of Public Health Sciences, King's College London, Capital House, 42 Weston Street, London SE1 3QD, UK.

出版信息

J Clin Epidemiol. 2004 Aug;57(8):785-94. doi: 10.1016/j.jclinepi.2003.12.013.

Abstract

OBJECTIVE

To provide information concerning the magnitude of the intraclass correlation coefficient (ICC) for cluster-based studies set in primary care.

STUDY DESIGN AND SETTING

Reanalysis of data from 31 cluster-based studies in primary care to estimate intraclass correlation coefficients from random effects models using maximum likelihood estimation.

RESULTS

ICCs were estimated for 1,039 variables. The median ICC was 0.010 (interquartile range [IQR] 0 to 0.032, range 0 to 0.840). After adjusting for individual- and cluster-level characteristics, the median ICC was 0.005 (IQR 0 to 0.021). A given measure showed widely varying ICC estimates in different datasets. In six datasets, the ICCs for SF-36 physical functioning scale ranged from 0.001 to 0.055 and for SF-36 general health from 0 to 0.072. In four datasets, the ICC for systolic blood pressure ranged from 0 to 0.052 and for diastolic blood pressure from 0 to 0.108.

CONCLUSION

The precise magnitude of between-cluster variation for a given measure can rarely be estimated in advance. Studies should be designed with reference to the overall distribution of ICCs and with attention to features that increase efficiency.

摘要

目的

提供有关基层医疗中基于聚类研究的组内相关系数(ICC)大小的信息。

研究设计与背景

对来自31项基层医疗中基于聚类研究的数据进行重新分析,以使用最大似然估计从随机效应模型中估计组内相关系数。

结果

对1039个变量估计了ICC。ICC的中位数为0.010(四分位间距[IQR]为0至0.032,范围为0至0.840)。在调整个体和聚类水平特征后,ICC的中位数为0.005(IQR为0至0.021)。给定的指标在不同数据集中显示出广泛不同ICC估计值。在六个数据集中,SF-36身体功能量表的ICC范围为0.001至0.055,SF-36总体健康的ICC范围为0至0.072。在四个数据集中,收缩压的ICC范围为0至0.052,舒张压的ICC范围为0至0.108。

结论

给定指标的聚类间变异的确切大小很少能预先估计。研究设计应参考ICC的总体分布,并关注提高效率的特征。

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