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Intermediate and advanced topics in multilevel logistic regression analysis

Austin, Peter C (författare)
University of Toronto
Merlo, Juan (författare)
Lund University,Lunds universitet,Socialepidemiologi,Forskargrupper vid Lunds universitet,Social Epidemiology,Lund University Research Groups
 (creator_code:org_t)
2017-05-23
2017
Engelska.
Ingår i: Statistics in Medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 36:20, s. 3257-3277
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)

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Av författaren/redakt...
Austin, Peter C
Merlo, Juan
Om ämnet
NATURVETENSKAP
NATURVETENSKAP
och Matematik
och Sannolikhetsteor ...
MEDICIN OCH HÄLSOVETENSKAP
MEDICIN OCH HÄLS ...
och Hälsovetenskap
och Folkhälsovetensk ...
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Statistics in Me ...
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Lunds universitet

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