Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies.

Ahmed ElhakeemRachael A HughesKate TillingDiana L CousminerStefan A JackowskiTim J ColeAlex S F KwongZheyuan LiStruan F A GrantAdam D G Baxter-JonesBabette S ZemelDeborah A Lawlor

Published in: BMC medical research methodology (2022)

LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.

Keyphrases

depressive symptoms
genome wide
big data
gene expression
decision making
dna methylation
single cell
neural network