Accessible analysis of longitudinal data with linear mixed effects models

• Published in: Disease models & mechanisms Vol. 15; no. 5
• Main Authors: Murphy, Jessica I, Weaver, Nicholas E, Hendricks, Audrey E
• Format: Journal Article
• Language: English
• Published: England The Company of Biologists Ltd 01.05.2022; The Company of Biologists
• Subjects: Alzheimer's disease; Animals; anova; Computer Simulation; Datasets; linear mixed effects; Linear Models; longitudinal; Longitudinal Studies; Mice; Microbiome; Microbiomes; Microbiota; Models, Statistical; mouse; Resource; shiny app; Statistical analysis; Statistical models; Variables; Linear mixed effects; Longitudinal; Mouse; ANOVA; Shiny app; Microbiome
• ISSN: 1754-8403; 1754-8411
• DOI: 10.1242/dmm.048025

Longitudinal studies are commonly used to examine possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the linear mixed effects (LME) model and how to use it for longitudinal studies. We re-analyze a dataset published by Blanton et al. in 2016 that modeled growth trajectories in mice after microbiome implantation from nourished or malnourished children. We compare the fit and stability of different parameterizations of ANOVA and LME models; most models found that the nourished versus malnourished growth trajectories differed significantly. We show through simulation that the results from the two-way ANOVA and LME models are not always consistent. Incorrectly modeling correlated data can result in increased rates of false positives or false negatives, supporting the need to model correlated data correctly. We provide an interactive Shiny App to enable accessible and appropriate analysis of longitudinal data using LME models.