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

BACKGROUND: Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory....

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Detalles Bibliográficos
Autores principales: Elhakeem, Ahmed, Hughes, Rachael A., Tilling, Kate, Cousminer, Diana L., Jackowski, Stefan A., Cole, Tim J., Kwong, Alex S. F., Li, Zheyuan, Grant, Struan F. A., Baxter-Jones, Adam D. G., Zemel, Babette S., Lawlor, Deborah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925070/
https://www.ncbi.nlm.nih.gov/pubmed/35291947
http://dx.doi.org/10.1186/s12874-022-01542-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8925070/
https://www.ncbi.nlm.nih.gov/pubmed/35291947
http://dx.doi.org/10.1186/s12874-022-01542-8

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