Predictive Cross-validation for the Choice of Linear Mixed-Effects Models with Application to Data from the Swiss HIV Cohort Study
Version of Record online: 6 JUL 2011
© 2011, The International Biometric Society
Volume 68, Issue 1, pages 53–61, March 2012
How to Cite
Braun, J., Held, L. and 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: 53–61. doi: 10.1111/j.1541-0420.2011.01621.x
- Issue online: 23 MAR 2012
- Version of Record online: 6 JUL 2011
- Received February 2010. Revised March 2011. Accepted April 2011.
- Linear mixed-effects model;
- Predictive model choice;
- Proper scoring rules;
- Serial correlation
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.