Generalized additive models to analyze nonlinear trends in biomedical longitudinal data using R: Beyond repeated measures ANOVA and linear mixed models

• Published in: Statistics in medicine Vol. 41; no. 21; pp. 4266 - 4283
• Main Authors: Mundo, Ariel I., Tipton, John R., Muldoon, Timothy J.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.09.2022
• Subjects: Analysis of Variance; biomedical data; Biomedical Research; generalized additive models; Humans; Linear Models; longitudinal data; Longitudinal Studies; Research Design; simulation; Trends; Variance analysis; Within-subjects design; R; biomedical data; generalized additive models; simulation; longitudinal data

In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm‐ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm‐ANOVA as they can work with unbalanced data and non‐const...