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

TitleLongitudinal variable selection by cross-validation in the case of many covariates.
Publication TypeJournal Article
Year of Publication2007
AuthorsCantoni, E, Field, C, J. Fleming, M, Ronchetti, E
JournalStat Med
Date Published2007 Feb 20
ISSN Number0277-6715
Call Numbernewpubs20070125_Cantoni_etal
KeywordsCohort Studies, Computer Simulation, Female, Humans, Linear Models, Longitudinal Studies, Male, Markov chains, Monte Carlo Method, Smoking, Socioeconomic factors

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.

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Alternate JournalStat Med
Citation Key7126
PubMed ID16625521