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PubMed · 16011698

Variable selection for marginal longitudinal generalized linear models.

Eva Cantoni·Joanna Mills Flemming·Elvezio Ronchetti
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Abstract

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GC(p).

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BiasBiometryComputer SimulationData Interpretation, StatisticalFemaleHumansLikelihood FunctionsLinear ModelsLongitudinal StudiesMaleModels, StatisticalRegression AnalysisResearch DesignStatistics, NonparametricJournal ArticleResearch Support, Non-U.S. Gov't

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Eva Cantoni, Joanna Mills Flemming, Elvezio Ronchetti. 2005. Variable selection for marginal longitudinal generalized linear models.. https://doi.org/10.1111/j.1541-0420.2005.00331.x

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