Using generalized additive models to analyze biomedical non-linear longitudinal data
A. I. Mundo, J. R. Tipton, T. J. Muldoon
This paper demonstrates the use of generalized additive models (GAMs) to analyze non-linear longitudinal biomedical data, showing their superiority over linear methods like LMEMs and rm-ANOVA in handling non-linear trends and missing data.
bioRxiv · Jun 10, 2021Cancer Biology