September is the start of the new term, and we'll kick off with what (in my opinion) is a very interesting topic. Alexina Mason (Imperial College) will speak about full Bayesian methods to deal with missing data. The seminar will be on September 18th at 4pm in the department of Statistical Science.
Title: A general strategy for dealing with missing data using Bayesian methods
Abstract: Bayesian full probability modelling provides a flexible approach for analysing data with missing values, and offers an alternative to standard multiple imputation. Plausible models allowing for missing responses and/or missing covariates can be built, which incorporate realistic assumptions about the missingness mechanism. Additionally, the Bayesian approach lends itself naturally to sensitivity analysis, which is crucial when the missingness mechanism is unknown. These strengths will be demonstrated by presenting a general strategy for a "statistically principled" investigation of data which contain missing values. The first part of this strategy entails constructing a "base model" by selecting an analysis model, then adding a sub-model to impute the missing covariates followed by a sub-model to allow informative missingness in the response. The second part involves running a series of sensitivity analyses to check the robustness of the conclusions. An antidepressant trial comparing the effects of three treatments will be used as an illustrative example throughout. In particular, we will focus on missing responses assuming a non-ignorable missingness mechanism.