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Cited by 649

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Intermediate and advanced topics in multilevel logistic regression analysis

Open access
Austin, Peter C., Merlo, Juan·2017·Statistics in Medicine
DOI 10.1002/sim.7336
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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 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 R2 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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Metadata

Authors
Austin, P. C., & Merlo, J.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2017-05-23
Volume
36
Issue
20
Pages
3257-3277
ISSN
0277-6715 (ISSN-L)
DOI
10.1002/sim.7336
OpenAlex
W2618058430
Cited by
649
References
52
FWCI
28.84
Type
journal article
Language
en
Open access
hybrid

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