Virtual discussion meeting: Linear mixed effects models for non-Gaussian continuous repeated measurement data

 
Share Download as iCal file
Click the icons above to Share, Tweet or add this event to your calendar (iCal)
 
RSS discussion paper meetings

Wednesday 13 May 2020

 
Location Online, details to be announced

This discussion meeting will be held virtually; we will confirm details nearer the time.

The Discussion paper ‘Linear mixed effects models for non-Gaussian continuous repeated measurement data’ will be presented by the authors, Ozgur Asar, David Bolin, Peter J Diggle and Jonas Wallin.

The preprint for the paper is available below and we welcome your contributions in the usual way. 

Ozgur Asar (Ac?badem Mehmet Ali Ayd?nlar University, Istanbul), David Bolin (King Abdullah University of Science and Technology, Thuwal, and University of Gothenburg), Peter J. Diggle (Lancaster University) and Jonas Wallin (Lund University)
‘Linear mixed effects models for non-Gaussian continuous repeated measurement data’

We consider the analysis of continuous repeated measurement outcomes that are collected longitudinally. A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time-varying random effects, and measurement noise. We develop methodology that, for the first time, allows any combination of these stochastic components to be non-Gaussian, using multivariate normal variance–mean mixtures. To meet the computational challenges that are presented by large data sets, i.e. in the current context, data sets with many subjects and/or many repeated measurements per subject, we propose a novel implementation of maximum likelihood estimation using a computationally efficient subsampling-based stochastic gradient algorithm. We obtain standard error estimates by inverting the observed Fisher information matrix and obtain the predictive distributions for the random effects in both filtering (conditioning on past and current data) and smoothing (conditioning on all data) contexts. To implement these procedures, we introduce an R package: ngme. We reanalyse two data sets, from cystic fibrosis and nephrology research, that were previously analysed by using Gaussian linear mixed effects models.

To be published in Series C; for more information go to the Wiley Online Library

The preprint is available to download.
Linear mixed effects models for non-Gaussian continuous repeated measurement data

Contact Judith Shorten

Organiser Name Judith Shorten

Email Address This email address is being protected from spambots. You need JavaScript enabled to view it.

Organising Group(s) Research Section of the Royal Statistical Society

 

 

 

 

Join the RSS

Join the RSS

Become part of an organisation which works to advance statistics and support statisticians

Copyright 2019 Royal Statistical Society. All Rights Reserved.
12 Errol Street, London, EC1Y 8LX. UK registered charity in England and Wales. No.306096

Twitter Facebook YouTube RSS feed RSS feed RSS newsletter

We use cookies to understand how you use our site and to improve your experience. By continuing to use our site, you accept our use of cookies and Terms of Use.