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Date: November 11, 2016
Author: Gordana Popovic
In linear models, the interpretation of model parameters is linear. For example, if a you were modelling plant height against altitude and your coefficient for altitude was -0.9, then plant height will decrease by 1.09 for every increase in altitude of 1 unit.
For generalised linear models, the interpretation is not this straightforward.
This is an archive of an external source
Date: November 13, 2017
Author: Kert Viele
Hierarchical modeling is a powerful tool for making inferences about multiple groups in clinical trials, whether these groups are multiple indications or tumor types in an oncology study, multiple sites in a device clinical trial, or any other source of patient heterogeneity.
Hierarchical models that share information across groups form a backbone of modern Bayesian thinking.