An iterative algorithm for joint covariate and random effect selection in mixed effects models

Delattre Maud () and Poursat Marie-Anne ()
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Delattre Maud: UMR MIA-Paris, AgroParisTech, INRAE, Université Paris-Saclay, 75005, Paris, France
Poursat Marie-Anne: Université Paris-Saclay, CNRS, INRIA, Laboratoire de mathématiques d'Orsay, 91405, Orsay, France

The International Journal of Biostatistics, 2020, vol. 16, issue 2, 12

Abstract: We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

Keywords: bayesian information criterion; joint covariate and random effects selection; nonlinear mixed effects models; stepwise procedure (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:16:y:2020:i:2:p:12:n:5

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DOI: 10.1515/ijb-2019-0082

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