Overview

While the term "extended regression model" (ERM) may be new, the method is not. ERMs are regression models with continuous outcomes (including censored and tobit outcomes), binary outcomes, and ordered outcomes that are fit via maximum likelihood and that also account for endogenous covariates, sample selection, and nonrandom treatment assignment. These models can be used when you are worried about bias due to unmeasured confounding, trials with informative dropout, outcomes that are missing not at random, selection on unobservables, and more. ERMs provide a unifying framework for handling these complications individually or in combination.

Join Charles Lindsey, Senior Statistician and Software Developer, as he briefly reviews the types of complications that ERMs can address. He will work through examples that demonstrate several of these complications and show some inferences we can make despite those complications.

How to join

The webinar is free, but you must register to attend. Registrations are limited so register soon.

We will send you an email prior to the start of the course with instructions on how to access the webinar. You will need access to Adobe Connect to attend.

Don't miss this opportunity to learn about ERMs from the experts.