Martin, I, Uh, H-W, Supali, T et al. (2 more authors) (2019) The mixed model for the analysis of a repeated‐measurement multivariate count data. Statistics in Medicine, 38 (12). pp. 2248-2268. ISSN 0277-6715
Abstract
Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. In addition, the effects of covariates on the multivariate counts distribution need to be assessed. To fulfill these requirements, a regression model based on the Dirichlet‐multinomial distribution for association between covariates and the categorical counts is extended by using random effects to deal with the additional clustering. This model is the Dirichlet‐multinomial mixed regression model. Alternatively, a negative binomial regression mixed model can be deployed where the corresponding likelihood is conditioned on the total count. It appears that these two approaches are equivalent when the total count is fixed and independent of the random effects. We consider both subject‐specific and categorical‐specific random effects. However, the latter has a larger computational burden when the number of categories increases. Our work is motivated by microbiome data sets obtained by sequencing of the amplicon of the bacterial 16S rRNA gene. These data have a compositional structure and are typically overdispersed. The microbiome data set is from an epidemiological study carried out in a helminth‐endemic area in Indonesia. The conclusions are as follows: time has no statistically significant effect on microbiome composition, the correlation between subjects is statistically significant, and treatment has a significant effect on the microbiome composition only in infected subjects who remained infected.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0/. |
Keywords: | conditional model; count; Dirichlet‐multinomial; generalized linear mixed model; microbiome; multivariate; overdispersion |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Jan 2019 10:22 |
Last Modified: | 25 Jun 2023 21:40 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/sim.8101 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141379 |
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