Accessible analysis of longitudinal data with linear mixed effects models

Abstract

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.

Small for gestational age and anthropometric body composition from early childhood to adulthood: the Aboriginal Birth Cohort study

“…Longitudinal analyses used linear mixed models to assess changes in anthropometric measures over time and differences between SGA and non-SGA and remote and urban residents. Random intercepts and slopes were included for each study participant to account for repeated measures ( 19 ). All anthropometric measures were standardised as z-scores (calculated internally across all waves combined) and were entered into models as outcomes regressed on sex (reference = male), age (with a polynomial cubed term), SGA status (reference = non-SGA), and geographic location (reference = remote).…”

Normalized Clinical Severity Scores Reveal a Correlation between X Chromosome Inactivation and Disease Severity in Rett Syndrome

“…Participants were also restricted to those with visits between 2 and 25 years of age to account for low numbers of observations beyond this window. A total of 1178 individuals met these requirements for downstream analysis (1003 with a diagnosis of "Classic" and 175 with a diagnosis of "Atypical"; see A mixed effects model was chosen for further analysis to accommodate the longitudinal nature of the data and between-subject differences in observation number and interval of observation [31]. Exploratory data analysis indicated CSS progression follows a logarithmic growth-like pattern (Supplementary Figure S1), so age was log transformed to accommodate this observation.…”