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

Statistics in Medicine
Eva CantoniElvezio Ronchetti

Abstract

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.

References

Jan 27, 1999·Journal of Health and Social Behavior·L A WrayR B Wallace
Jul 14, 2005·Biometrics·Eva CantoniElvezio Ronchetti

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Citations

Oct 29, 2015·Biometrical Journal. Biometrische Zeitschrift·Masahiko Gosho
Jul 6, 2016·Frontiers in Genetics·Stéphane GuerrierYanyuan Ma
Jun 13, 2014·Statistical Methods in Medical Research·Jeevanantham Rajeswaran, Eugene H Blackstone
Jan 24, 2018·BMC Bioinformatics·Michail TsagrisIoannis Tsamardinos
Jan 1, 2010·Research Synthesis Methods·Larry V HedgesMatthew C Johnson
Feb 28, 2014·Biometrics·Peirong XuYi Li

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