Full course description
Term: Spring 2020
Date: May 8th, 2020
Time: 12:00pm - 1:30pm
Location: ONLINE ONLY
Instructor: Leanna House, Jen Van Mullekom & Katherine Miller
Presented By: This course is jointly sponsored by the Statistical Applications and Innovations Group (SAIG) and the Integrated Translational Health Research Institute of Virginia (iTHRIV).
In this webinar, we will go back to the basics when considering methods in statistics to make inference, and progress to advanced examples of Bayesian statistics within healthcare. The objective is to define Bayesian ideas and terms so that articles which rely on Bayesian methods are accessible. Specifically, we will start by reminding ourselves of basic assumptions upon which we rely to communicate judgements supported by data with uncertainty. From classical (often referenced as frequentist) and Bayesian perspectives, these basic assumptions differ and thus influence analytic choices in all of the following: available sources of information for estimating parameters; models used to describe natural phenomena; mathematical (thus computational) approaches for estimating parameters in those models; methods for communicating uncertainty; and more. To conclude the webinar, examples in healthcare analytics that rely on Bayesian methods will be discussed. Though all of the technical underpinnings of these examples will not be highlighted, the use of advanced Bayes will