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Intermediate and advanced topics in multilevel logistic regression analysis
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575471/ https://www.ncbi.nlm.nih.gov/pubmed/28543517 http://dx.doi.org/10.1002/sim.7336 |
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author | Austin, Peter C. Merlo, Juan |
author_facet | Austin, Peter C. Merlo, Juan |
author_sort | Austin, Peter C. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5575471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55754712017-09-15 Intermediate and advanced topics in multilevel logistic regression analysis Austin, Peter C. Merlo, Juan Stat Med Tutorial in Biostatistics 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. John Wiley and Sons Inc. 2017-05-23 2017-09-10 /pmc/articles/PMC5575471/ /pubmed/28543517 http://dx.doi.org/10.1002/sim.7336 Text en © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Tutorial in Biostatistics Austin, Peter C. Merlo, Juan Intermediate and advanced topics in multilevel logistic regression analysis |
title | Intermediate and advanced topics in multilevel logistic regression analysis |
title_full | Intermediate and advanced topics in multilevel logistic regression analysis |
title_fullStr | Intermediate and advanced topics in multilevel logistic regression analysis |
title_full_unstemmed | Intermediate and advanced topics in multilevel logistic regression analysis |
title_short | Intermediate and advanced topics in multilevel logistic regression analysis |
title_sort | intermediate and advanced topics in multilevel logistic regression analysis |
topic | Tutorial in Biostatistics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5575471/ https://www.ncbi.nlm.nih.gov/pubmed/28543517 http://dx.doi.org/10.1002/sim.7336 |
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