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Generalized Additive Models for Longitudinal Biomedical Data

Ariel Mundo, a Ph.D. candidate at the Department of Biomedical Engineering at the University of Arkansas, presents on Generalized Additive Models for Longitudinal Biomedical Data. Mundo further describes how longitudinal data are used in biomedical research. A simulation of an rm-ANOVA model is also provided to give the audience a better understanding of the subject. Originally presented at R/Medicine 2021 by Ariel Mundo https://r-medicine.org Main Sections 0:00 Introduction 1:01 Motivation 1:32 How do we analyze longitudinal data? 1:52 Simulation 2:22 How does an rm-ANOVA model look on this data? 3:18 What exactly is an rm-ANOVA? 3:56 Examples 4:33 An alternative: Generalized additive models (GAMs) 6:05 How does the GAM model look for the simulated data? 7:50 Other advantages of GAMs 8:10 Conclusion and acknowledgments More Resources Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Blog: https://www.r-consortium.org/news/blog Join: https://www.r-consortium.org/about/join Twitter:   / rconsortium   LinkedIn:   / r-consortium