2 results match your criteria boosted glmm

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Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.

J Clin Transl Sci 2020 Nov 16;5(1):e59. Epub 2020 Nov 16.

Department of Medicine, Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC, USA.

Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized . We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Read More

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November 2020

Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c.

J Biomed Inform 2019 01 4;89:56-67. Epub 2018 Sep 4.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. Read More

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January 2019
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