Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22054

Title: Quantifying intraclass correlations for count and time-to-event data
Authors: Oliveira, Izabela R. C.
Molenberghs, Geert
Demetrio, Clarice G. B.
Dias, Carlos T. S.
Giolo, Suely R.
Andrade, Marcela C.
Issue Date: 2016
Citation: BIOMETRICAL JOURNAL, 58(4), p. 852-867
Abstract: The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.
Notes: [Oliveira, Izabela R. C.] Univ Fed Lavras, Dept Exact Sci, BR-37200000 Lavras, Brazil. [Oliveira, Izabela R. C.; Molenberghs, Geert] Univ Hasselt, BioStat 1, B-3500 Hasselt, Belgium. [Oliveira, Izabela R. C.; Demetrio, Clarice G. B.; Dias, Carlos T. S.] ESALQ USP, Dept Exact Sci, BR-13418900 Piracicaba, Brazil. [Molenberghs, Geert] Katholieke Univ Leuven, BioStat 1, B-3000 Leuven, Belgium. [Giolo, Suely R.] Univ Fed Parana, Dept Stat, BR-80060000 Curitiba, Parana, Brazil. [Andrade, Marcela C.] Univ Fed Lavras, Dept Biol, BR-37200000 Lavras, Brazil.
URI: http://hdl.handle.net/1942/22054
DOI: 10.1002/bimj.201500093
ISI #: 000379929300008
ISSN: 0323-3847
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version141.79 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.