Many longitudinal studies attempt to examine changes in outcome measures over time in groups of patients.
Applying conventional analytic techniques, such as a single classical linear regression model, to these data will often not result in minimum variance estimates, and may affect the results of tests of significance.
Pooled time series regression analyses comprise a set of techniques that may be used in these instances to model changes in outcome measures over time.
Pooling of time series data from many individuals may be done using two types of models: fixed effect models, which specify differences among individuals in separate intercept terms, and random effects models, which allow for differences among individuals by including an additional error component in the model.
Mots-clés Pascal : Epidémiologie, Analyse régression, Pool, Etude longitudinale, Méthodologie, Série temporelle, Modèle statistique
Mots-clés Pascal anglais : Epidemiology, Regression analysis, Pool, Follow up study, Methodology, Time series, Statistical model
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 93-0539221
Code Inist : 002B30A01A1. Création : 199406.