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...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
BMC
2022-06-01
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Series: | BMC Medical Research Methodology |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12874-022-01641-6 |