This is a past event
RSS Highlands Local Group - invited speaker
Multiple Imputation (MI) is a flexible tool to handle missing data that has been increasingly used in recent years. It broadly consists in filling in the missing values multiple times, creating several completed datasets; these are then analysed with standard techniques obtaining different parameter estimates, that are finally combined with Rubin’s rules. One of the conditions for the validity of MI is that the two models used for (i) imputing and (ii) analysing the data need to be compatible. For this reason, when the partially observed data have a multilevel structure, both models need to reflect this. In this talk I am going to present an imputation technique, known as Joint Modelling imputation, based on running an MCMC sampler after defining a joint imputation model for the partially observed variables. This imputation strategy is particularly appealing for imputing data compatibly with a multilevel analysis model and is implemented in the R package jomo. I will explain under which circumstances simple JM imputation works properly, and I will explore possible solutions to situations where it doesn’t. Finally I will conclude by outlining plans for future research in this area.
RSS HLG look forward to seeing you there!
- Speaker
- Matteo Quartagno, Research Fellow, Dept of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine
- Hosted by
- RSS Highland
- Venue
- Room 115, Health Sciences Building, Foresterhill, University of Aberdeen