Scientific article

Longitudinal variable selection by cross-validation in the case of many covariates

Published inStatistics in medicine, vol. 26, no. 4, p. 919-930
Publication date2007

Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (Marginal Models, Mixed Effects Models, etc.), far fewer options exist for the closely related issue of variable selection. In addition, longitudinal data typically derive from medical or other large-scale studies where often large numbers of potential explanatory variables and hence even larger numbers of candidate models must be considered. Cross-validation is a popular method for variable selection based on the predictive ability of the model. Here, we propose a cross-validation Markov Chain Monte Carlo procedure as a general variable selection tool which avoids the need to visit all candidate models. Inclusion of a “one-standard error” rule provides users with a collection of good models as is often desired. We demonstrate the effectiveness of our procedure both in a simulation setting and in a real application.

Citation (ISO format)
CANTONI, Eva et al. Longitudinal variable selection by cross-validation in the case of many covariates. In: Statistics in medicine, 2007, vol. 26, n° 4, p. 919–930. doi: 10.1002/sim.2572
Main files (1)
Article (Accepted version)
ISSN of the journal0277-6715

Technical informations

Creation09/14/2012 11:57:00 AM
First validation09/14/2012 11:57:00 AM
Update time03/14/2023 5:41:00 PM
Status update03/14/2023 5:41:00 PM
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