Abstract
Generalized linear mixed models are suitable for analyzing data with multiple categorical variables, both outcome and exposure variables. Do random intercept versions of these models provide better sensitivity of testing than fixed intercept models.
This chapter was previously published in “Machine learning in medicine-cookbook 2” as Chap. 6, 2014.
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Cleophas, T.J., Zwinderman, A.H. (2020). Random Intercept Models for Both Outcome and Predictor Categories (55 Patients). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_30
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DOI: https://doi.org/10.1007/978-3-030-33970-8_30
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