ArticleInternational statistical review = Revue internationale de statistique2014
A unifying framework for marginalized random intercept models of correlated binary outcomes.
Article in International statistical review = Revue internationale de statistique, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Marginally Interpretable Spatial Logistic Regression With Bridge Processes.Statistics in medicine · 2026Article
- Bridged parametric survival models: General paradigm and speed improvements.Computer methods and programs in biomedicine · 2021Article
- A Bayesian multivariate latent t-regression model for assessing the association between corticosteroid and cranial radiation exposures and cardiometabolic complications in survivors of childhood acute lymphoblastic leukemia: a PETALE study.BMC medical research methodology · 2019Article
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3 authors.
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Abstract
We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized random intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.
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Registered trials
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