Open AccessAccessible analysis of longitudinal data with linear mixed effects models
Author(s) -
Jessica I. Murphy,
Nicholas E. Weaver,
Audrey E. Hendricks
Publication year - 2022
Publication title -
disease models and mechanisms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.327
H-Index - 83
eISSN - 1754-8411
pISSN - 1754-8403
DOI - 10.1242/dmm.048025
Subject(s) - longitudinal data , false positive paradox , mixed model , statistics , linear model , analysis of variance , repeated measures design , generalized linear mixed model , stability (learning theory) , econometrics , variance (accounting) , longitudinal study , statistical model , computer science , mathematics , machine learning , data mining , accounting , business
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.