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Generalized Linear Models and Its Extensions

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Abstract

This chapter introduces how to analyze nonstandard data types, like binary, categorical, ordinal, and time to event data through generalized linear models (GLMs) and their extension. Logistic regression of binary or ordinal data, Poisson regression of count data, beta regression of proportions, and parametric modeling of survival data are discussed, as are generalized estimating equations. GLMs are then extended to nonlinear GLMs. Three case studies are presented: analysis of adverse event data from a clinical trial, analysis of the frequency of seizure data, and ordinal regression analysis of neutropenia and time to event data from a clinical study with a new anticancer drug.

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Notes

  1. 1.

    I have to admit that in the first edition of this book in the chapter on Case Studies in Linear and Nonlinear Regression in the example on logistic regression, I made the common misinterpretation of the OR as relative risk.

  2. 2.

    An example of clustering is when observations are collected across multiple sites but subjects within a site are correlated. This might occur because particular physicians may treat patients a particular way or patients may receive better care within a particular hospital leading to site differences in response.

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  1. Astellas Pharma Global Development Pharmacokinetics, Modeling, and Simulation, Deerfield, IL, USA

    Peter L. Bonate Ph.D.

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Bonate, P.L. (2011). Generalized Linear Models and Its Extensions. In: Pharmacokinetic-Pharmacodynamic Modeling and Simulation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9485-1_11

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