Penalized generalized estimating equations for relative risk regression with applications to brain lesion data

Abstract

Motivated by a brain lesion application, we introduce penalized generalized estimating equations for relative risk regression for modelling correlated binary data. Brain lesions can have varying incidence across the brain and result in both rare and high incidence outcomes. As a result, odds ratios estimated from generalized estimating equations with logistic regression structures are not necessarily directly interpretable as relative risks. On the other hand, use of log-link regression structures with the binomial variance function may lead to estimation instabilities when event probabilities are close to 1. To circumvent such issues, we use generalized estimating equations with log-link regression structures with identity variance function and unknown dispersion parameter. Even in this setting, parameter estimates can be infinite, which we address by penalizing the generalized estimating functions with the gradient of the Jeffreys prior. Our findings from extensive simulation studies show significant improvement over the standard log-link generalized estimating equations by providing finite estimates and achieving convergence when boundary estimates occur. The real data application on UK Biobank brain lesion maps further reveals the instabilities of the standard log-link generalized estimating equations for a large-scale data set and demonstrates the clear interpretation of relative risk in clinical applications.