Header

UZH-Logo

Maintenance Infos

Quasi-complete separation in random effects of binary response mixed models


Sauter, Rafael; Held, Leonhard (2016). Quasi-complete separation in random effects of binary response mixed models. Journal of Statistical Computation and Simulation, 86(14):2781-2796.

Abstract

Clustered observations such as longitudinal data are often analysed with generalized linear mixed models (GLMM). Approximate Bayesian inference for GLMMs with normally distributed random effects can be done using integrated nested Laplace approximations (INLA), which is in general known to yield accurate results. However, INLA is known to be less accurate for GLMMs with binary response. For longitudinal binary response data it is common that patients do not change their health state during the study period. In this case the grouping covariate perfectly predicts a subset of the response, which implies a monotone likelihood with diverging maximum likelihood (ML) estimates for cluster-specific parameters. This is known as quasi-complete separation. In this paper we demonstrate, based on longitudinal data from a randomized clinical trial and two simulations, that the accuracy of INLA decreases with increasing degree of cluster-specific quasi-complete separation. Comparing parameter estimates by INLA, Markov chain Monte Carlo sampling and ML shows that INLA increasingly deviates from the other methods in such a scenario.

Abstract

Clustered observations such as longitudinal data are often analysed with generalized linear mixed models (GLMM). Approximate Bayesian inference for GLMMs with normally distributed random effects can be done using integrated nested Laplace approximations (INLA), which is in general known to yield accurate results. However, INLA is known to be less accurate for GLMMs with binary response. For longitudinal binary response data it is common that patients do not change their health state during the study period. In this case the grouping covariate perfectly predicts a subset of the response, which implies a monotone likelihood with diverging maximum likelihood (ML) estimates for cluster-specific parameters. This is known as quasi-complete separation. In this paper we demonstrate, based on longitudinal data from a randomized clinical trial and two simulations, that the accuracy of INLA decreases with increasing degree of cluster-specific quasi-complete separation. Comparing parameter estimates by INLA, Markov chain Monte Carlo sampling and ML shows that INLA increasingly deviates from the other methods in such a scenario.

Statistics

Citations

3 citations in Web of Science®
1 citation in Scopus®
Google Scholar™

Altmetrics

Downloads

5 downloads since deposited on 29 Nov 2016
5 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2016
Deposited On:29 Nov 2016 13:31
Last Modified:07 Dec 2016 15:19
Publisher:Taylor & Francis
ISSN:0094-9655
Additional Information:This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation on 2016, available online: http://wwww.tandfonline.com/10.1080/00949655.2015.1129539
Publisher DOI:https://doi.org/10.1080/00949655.2015.1129539

Download

Preview Icon on Download
Preview
Content: Accepted Version
Filetype: PDF
Size: 484kB
View at publisher
Preview Icon on Download
Preview
Content: Accepted Version
Filetype: PDF (Supplementary material)
Size: 418kB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations