Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data
Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data
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DOI：
10.1002/sim.1524
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发表时间：
2003-08-30
影响因子：
2
通讯作者：
Parides, MK
中科院分区：
文献类型：
--
作者：
Begg, MD;Parides, MK
本文的研究重点是聚类数据的回归分析。虽然集群内相关性的存在(集群内的项目反应相同的趋势)通常被视为良好推理的障碍，但集群数据的复杂结构提供了相对于独立数据的显著分析优势。一个关键的优势是能够在个人(或特定于物品)级别和组(或特定于集群)级别区分效果。我们回顾了分离个体水平和集群水平对反应的影响的不同方法，以及它们的适当解释，并根据数据分析师的意图给出了模型拟合的建议。与许多早期关于这一主题的论文不同，我们特别强调对簇级协变量效应的解释。这篇论文的主要观点是利用一项大型出生队列研究中的兄弟姐妹数据对出生体重和智商之间的关系进行分析。版权所有(C)2003 John Wiley Sons，Ltd.
The focus of this paper is regression analysis of clustered data. Although the presence of intracluster correlation (the tendency for items within a cluster to respond alike) is typically viewed as an obstacle to good inference, the complex structure of clustered data offers significant analytic advantages over independent data. One key advantage is the ability to separate effects at the individual (or item-specific) level and the group (or cluster-specific) level. We review different approaches for the separation of individual-level and cluster-level effects on response, their appropriate interpretation and give recommendations for model fitting based on the intent of the data analyst. Unlike many earlier papers on this topic, we place particular emphasis on the interpretation of the cluster-level covariate effect. The main ideas of the paper are highlighted in an analysis of the relationship between birth weight and IQ using sibling data from a large birth cohort study. Copyright (C) 2003 John Wiley Sons, Ltd.