Log-binomial models: exploring failed convergence. - Williamson, Tyler.; Eliasziw, Misha.; Fick, Gordon Hilton. (2016)
Background: Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using log-binomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software may report failed convergence when attempting to fit log-binomial models in certain settings. The methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue. This research looks directly at the log-likelihood function for the simplest log-binomial model where failed convergence has been observed, a model with a single linear predictor with three levels. The possible causes of failed convergence are explored and potential solutions are presented for some cases.; Keywords: Log-binomial, Non-convergence, Failed convergence, Relative risk, Method of maximum likelihood, Log relative risk, Likelihood estimation, Maximum likelihood estimates, Logistic regression alternatives.; Springer Open.