Come and hear me talk about emerging work with Bayesian latent variable models and SEMs. email Luluwah al-Fagih if you want to attend: L.Al-Fagih@kingston.ac.uk
Applying Bayesian latent variable models to imperfect healthcare data
Abstract: Analysis routinely collected or observational healthcare data is increasingly popular but troubled by poor data quality from a number of sources. Human error, coding habits, missing and coarse data all play a part in this. I will describe the application of Bayesian latent variable models to tackle issues like these in various forms to four projects: a pilot clinical trial in stroke rehabilitation, a meta-analysis including mean differences in depression scores alongside odds of reaching a threshold, an exploratory study of predictors of ocular tuberculosis, and an observational study of the timing of imaging in initial treatment of major trauma patients. The motivation for the Bayesian approach is the ease of flexible modelling, and I will explain the choices of software and algorithms currently available. Using latent variables allows us to draw inferences based on the unknown true values that are not available to us, while explicitly taking all sources of uncertainty into account.