Abstract
Multinomial regression is adequate for identifying the main predictors of certain outcome categories, like different levels of injury or quality of life (QOL) (see also Chap. 28). An alternative approach is logit loglinear modeling. The latter method does not use continuous predictors on a case by case basis, but rather the weighted means of these predictors. This approach may allow for relevant additional conclusions from your data.
This chapter was previously published in “Machine learning in medicine-cookbook 3” as Chap. 6, 2014.
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Cleophas, T.J., Zwinderman, A.H. (2020). Logit Loglinear and Hierarchical Loglinear Modeling for Outcome Categories (445 Patients). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_39
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DOI: https://doi.org/10.1007/978-3-030-33970-8_39
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