An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes

An investigation of penalization and data augmentation to improve convergence of generalized estimating equations for clustered binary outcomes

Abstract Background In binary logistic regression data are ‘separable’ if there exists a linear combination of explanatory variables which perfectly predicts the observed outcome, leading to non-existence of some of the maximum likelihood coefficient estimates. A popular solution to obtain finite es...

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Bibliographic Details
Main Authors: Angelika Geroldinger, Rok Blagus, Helen Ogden, Georg Heinze
Format: Article
Language:English
Published: BMC 2022-06-01
Series:BMC Medical Research Methodology
Subjects:
Clustered data
Firth’s logistic regression
Generalized estimating equations
Logistic regression
Non-convergence
Separation
Online Access:https://doi.org/10.1186/s12874-022-01641-6

Internet

https://doi.org/10.1186/s12874-022-01641-6

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