Unchanged cardiovascular and respiratory outcomes in healthy C57Bl/6 mice after <i>in utero</i> exposure to ionizing radiation

McEvoy-May, James H.; Jones, Devon; Stoa, Lisa; Dixon, Dani‐Louise; Tai, T.C.; Hooker, Antony M.; Boreham, Douglas R.; Wilson, Joanna Y.

doi:10.1080/09553002.2021.1855372

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…Additional structure could still exist within the data, such as autocorrelation, which would require further investigation. Generalized additive models could potentially be used to explain nonlinear patterns in the data given enough time points ( McEvoy-May et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Accessible analysis of longitudinal data with linear mixed effects models

Murphy

Weaver

Hendricks

2022

Disease Models &Amp; Mechanisms

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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.

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Section: Discussionmentioning

confidence: 99%