Skip to Main content Skip to Navigation
Journal articles

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

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.
Document type :
Journal articles
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03144783
Contributor : Marie-Anne Poursat <>
Submitted on : Wednesday, February 17, 2021 - 8:16:46 PM
Last modification on : Tuesday, June 15, 2021 - 2:57:34 PM

Links full text

Identifiers

Citation

Maud Delattre, Marie-Anne Poursat. An iterative algorithm for joint covariate and random effect selection in mixed effects models. International Journal of Biostatistics, De Gruyter, 2020, 16 (2), ⟨10.1515/ijb-2019-0082⟩. ⟨hal-03144783⟩

Share

Metrics

Record views

69