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Regression Analysis for Correlated Data

Regression Analysis for Correlated Data Regression analysis is among the most commonly used methods of statistical analysis in public health research. Its objective is to describe the relationship of a response with explanatory variables. One example of a regression problem is to identify factors associated with the racial difference in the risk of low birthweight (29). Regression includes the following as special cases: linear models for measured responses, logistic models for binary responses, and survival analyses for times to events. A basic assumption of regression analysis is that all observations are statistically independent, or at least uncorrelated with each other. In the low birthweight example, this assumption would mean that knowing one child's birthweight status provides no informa­ tion as to whether another child in the study has a low birthweight. One may argue that the assumption of independence is unlikely to be true if children of the same mother are included in the sample. Due to their common household environment and genes, we would expect a child to have a greater chance of having a low birthweight if his/her sibling had. Data from this hypothetical example can usefully be thought of as being "clustered" into families. Birth­ weights from different families are http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Public Health Annual Reviews

Regression Analysis for Correlated Data

Liang, K Y; Zeger, S L
Annual Review of Public Health , Volume 14 (1) – May 1, 1993
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Publisher
Annual Reviews
Copyright
Copyright 1993 Annual Reviews. All rights reserved
Subject
Review Articles
ISSN
0163-7525
eISSN
1545-2093
DOI
10.1146/annurev.pu.14.050193.000355
pmid
8323597
Publisher site
See Article on Publisher Site

Abstract

Regression analysis is among the most commonly used methods of statistical analysis in public health research. Its objective is to describe the relationship of a response with explanatory variables. One example of a regression problem is to identify factors associated with the racial difference in the risk of low birthweight (29). Regression includes the following as special cases: linear models for measured responses, logistic models for binary responses, and survival analyses for times to events. A basic assumption of regression analysis is that all observations are statistically independent, or at least uncorrelated with each other. In the low birthweight example, this assumption would mean that knowing one child's birthweight status provides no informa­ tion as to whether another child in the study has a low birthweight. One may argue that the assumption of independence is unlikely to be true if children of the same mother are included in the sample. Due to their common household environment and genes, we would expect a child to have a greater chance of having a low birthweight if his/her sibling had. Data from this hypothetical example can usefully be thought of as being "clustered" into families. Birth­ weights from different families are

Journal

Annual Review of Public Health – Annual Reviews

Published: May 1, 1993

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