Date: 2015-11-06

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Most regression models in biostatistics assume identifiability, which means that each point in the parameter space corresponds to a unique likelihood function for the observable data. Recently there has been interest in Bayesian inference for non-identifiable models, which can better represent uncertainty in some contexts. One example is in the field of epidemiology, where the investigator is concerned with bias due to unmeasured confounders (omitted variables). In this talk, I will illustrate Bayesian analysis of a non-identifiable model from epidemiology using government administrative data from British Columbia. I will show how to use the software STAN, which is new software developed by Andrew Gelman and others in the USA. STAN allows the careful study of posterior distributions in a vast collection of Bayesian models, including non-identifiable models for bias in epidemiology, which are poorly suited to conventional Gibbs sampling.

Speaker

Lawrence McCandless is an Associate Professor in the Faculty of Health Sciences at Simon Fraser University, Burnaby, BC. His broad research interests include Bayesian inference, causal inference, mediation analysis, meta-analysis, and survival analysis.