The complex structure of the longitudinal models

FreshBiostats

Two weeks ago, we started to talk in this blog about longitudinal data with the post by Urko Agirre. This type of data involves complex structure models called longitudinal models.

Longitudinal studies have two important characteristics:

  1. They are multivariate because for each studied individual many temporal measurements from the response variable (and covariates) are collected.

  2. They are multilevel as the variables measured are nested within the subjects under study, therefore resulting in layers.

These characteristics allow us to make inference about the general trend of the population as well as about the specific differences between subjects that can evolve in another way regarding the overall average behavior.

At the beginning of the 20th century this type of data started to be modelled. Different proposals appeared such as ANOVA models (Fisher, 1918), MANOVA models (generalised from ANOVA models to multivariate) or growth curves (Grizzle and Allen, 1969

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