unable to calculate standard error stata

UNABLE TO CALCULATE STANDARD ERROR STATA SOFTWARE

The majority of work to-date on log-binomial models has been focused on trying to find solutions to the observed problem of failed convergence. However, another reason that ORs are reported in inappropriate settings is the current perception that there is not a viable alternative to logistic regression (which provides ORs) for modelling risk, particularly one that offers RRs rather than ORs. In response to criticism of this practice, some would cite the well known fact that probability and odds are very close when the probability is itself small, the so-called rare-disease assumption. But ORs are also reported in settings where most epidemiologists would regard the RR as the preferred measure of association. In case-control studies, ORs remain definitive. ORs are frequently reported in a variety of settings. ConclusionsĮpidemiologists are encouraged to continue to use log-binomial models and advocate for improvements to the fitting algorithms to promote the widespread use of log-binomial models. Despite these limitations, log-binomial models are a viable option for epidemiologists wishing to describe the relationship between a set of predictors and a binary outcome where relative risk is the desired summary measure.

ResultsĪmong the principal causes is a failure of the fitting algorithm to converge despite the log-likelihood function having a single finite maximum. The possible causes of failed convergence are explored and potential solutions are presented for some cases. 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 methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue.

UNABLE TO CALCULATE STANDARD ERROR STATA SOFTWARE

However, standard statistical software may report failed convergence when attempting to fit log-binomial models in certain settings. 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. Relative risk is a summary metric that is commonly used in epidemiological investigations.

Unable to calculate standard error stata