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Intermediate and advanced topics in multilevel logistic regression analysis

Austin, Peter C and Merlo, Juan LU (2017) In Statistics in Medicine 36(20). p.3257-3277
Abstract

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement... (More)

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R(2) measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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organization
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type
Contribution to journal
publication status
published
subject
keywords
Journal Article
in
Statistics in Medicine
volume
36
issue
20
pages
3257 - 3277
publisher
John Wiley & Sons
external identifiers
  • scopus:85019753154
  • wos:000406833900010
ISSN
1097-0258
DOI
10.1002/sim.7336
language
English
LU publication?
yes
id
efb4b893-6888-4ec6-b519-77ae6e504ec7
date added to LUP
2017-05-31 07:03:15
date last changed
2018-08-19 04:35:01
@article{efb4b893-6888-4ec6-b519-77ae6e504ec7,
  abstract     = {<p>Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R(2) measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley &amp; Sons Ltd.</p>},
  author       = {Austin, Peter C and Merlo, Juan},
  issn         = {1097-0258},
  keyword      = {Journal Article},
  language     = {eng},
  month        = {05},
  number       = {20},
  pages        = {3257--3277},
  publisher    = {John Wiley & Sons},
  series       = {Statistics in Medicine},
  title        = {Intermediate and advanced topics in multilevel logistic regression analysis},
  url          = {http://dx.doi.org/10.1002/sim.7336},
  volume       = {36},
  year         = {2017},
}