The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods

Ethan M McCormick; Michelle L Byrne; John C Flournoy; Kathryn L Mills; Jennifer H Pfeifer

doi:10.1016/j.dcn.2023.101281

The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods

Dev Cogn Neurosci. 2023 Oct:63:101281. doi: 10.1016/j.dcn.2023.101281. Epub 2023 Jul 26.

Authors

Ethan M McCormick¹, Michelle L Byrne², John C Flournoy³, Kathryn L Mills⁴, Jennifer H Pfeifer⁴

Affiliations

¹ Methodology & Statistics Department, Institute of Psychology, Leiden University, Leiden, Netherlands; Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, United States; Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands. Electronic address: e.m.mccormick@fsw.leidenuniv.nl.
² Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Department of Psychology, University of Oregon, Eugene, United States.
³ Department of Psychology, Harvard University, Cambridge, United States.
⁴ Department of Psychology, University of Oregon, Eugene, United States.

Abstract

Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental neuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmental sciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginner-friendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications.

Keywords: Covariates; Distal outcomes; Longitudinal models; Mixed-effects models; Nonlinear trajectories; Structural equation models.