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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 estimates even with separable data is Firth’s logistic regression …