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Cited by 541

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Measuring explained variation in linear mixed effects models

Xu, Ronghui·2003·Statistics in Medicine
DOI 10.1002/sim.1572
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Abstract

We generalize the well‐known R2 measure for linear regression to linear mixed effects models. Our work was motivated by a cluster‐randomized study conducted by the Eastern Cooperative Oncology Group, to compare two different versions of informed consent document. We quantify the variation in the response that is explained by the covariates under the linear mixed model, and study three types of measures to estimate such quantities. The first type of measures make direct use of the estimated variances; the second type of measures use residual sums of squares in analogy to the linear regression; the third type of measures are based on the Kullback–Leibler information gain. All the measures can be easily obtained from software programs that fit linear mixed models. We study the performance of the measures through Monte Carlo simulations, and illustrate the usefulness of the measures on data sets. Copyright © 2003 John Wiley & Sons, Ltd.

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Show all 6 references

Research works printed but not linked: 29

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Metadata

Authors
Xu, R.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2003-10-29
Volume
22
Issue
22
Pages
3527-3541
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.1572
OpenAlex
W2072014595
Cited by
541
References
6
FWCI
2.93
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)

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Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine. https://doi.org/10.1002/sim.1572

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