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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-53041

Braun, J; Held, L; Ledergerber, B (2012). Predictive cross-validation for the choice of linear mixed-effects models with application to data from the Swiss HIV Cohort Study. Biometrics, 68(1):53-61.

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Summary Model choice in linear mixed-effects models for longitudinal data is a challenging task. Apart from the selection of covariates, also the choice of the random effects and the residual correlation structure should be possible. Application of classical model choice criteria such as Akaike information criterion (AIC) or Bayesian information criterion is not obvious, and many versions do exist. In this article, a predictive cross-validation approach to model choice is proposed based on the logarithmic and the continuous ranked probability score. In contrast to full cross-validation, the model has to be fitted only once, which enables fast computations, even for large data sets. Relationships to the recently proposed conditional AIC are discussed. The methodology is applied to search for the best model to predict the course of CD4+ counts using data obtained from the Swiss HIV Cohort Study.


4 citations in Web of Science®
5 citations in Scopus®
Google Scholar™



2 downloads since deposited on 29 Dec 2011
1 download since 12 months

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
04 Faculty of Medicine > University Hospital Zurich > Clinic for Infectious Diseases
Dewey Decimal Classification:610 Medicine & health
Deposited On:29 Dec 2011 13:31
Last Modified:05 Apr 2016 15:15
Publisher DOI:10.1111/j.1541-0420.2011.01621.x
PubMed ID:21732925

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