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 (FL), which was originally proposed to reduce the bias in coefficient estimates. The question of convergence becomes more involved when analyzing clustered data as frequently encountered in clinical research, e.g. data collected in several study centers or when individuals contribute multiple observations, using marginal logistic regression models fitted by generalized estimating equations (GEE). From our experience we suspect that s

SUBMITTER: Geroldinger A 

PROVIDER: S-EPMC9178839 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature