Abstract
If we have reasons to believe that in a study certain patients due to co-mobidity, co-medication and other factors will respond differently from others, then the spread in the data is caused not only by residual effect, but also by some subgroup property, otherwise called some random effect. Variance components analysis is able to assess the magnitudes of random effects as compared to that of the residual error of a study.
This chapter was previously published in “Machine learning in medicine-cookbook 3” as Chap. 3, 2014.
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Cleophas, T.J., Zwinderman, A.H. (2020). Variance Components for Assessing the Magnitude of Random Effects (40 Patients). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_36
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DOI: https://doi.org/10.1007/978-3-030-33970-8_36
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