Couverture de Clustered longitudinal data analysis
Titre du livre:

Clustered longitudinal data analysis

Extension and comparison of marginal models

LAP LAMBERT Academic Publishing (21-05-2010 )

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ISBN-13:

978-3-8383-1113-5

ISBN-10:
3838311132
EAN:
9783838311135
Langue du livre:
Anglais
texte du rabat:
Clustered longitudinal data are often collected as repeated measures on subjects arising in clusters. Examples include periodontal disease study, where the measurements related to the disease status of each tooth are collected over time for each patient which is considered as a cluster. For such applications, the number of teeth for each patient may be related to the overall oral health of the individual and hence may influence the distribution of the outcome measure of interest leading to an informative cluster size. Under such situations, three competing marginal linear models are proposed for clustered longitudinal data,namely, generalized estimating equations (GEE), within- cluster resampling (WCR), and cluster-weighted generalized estimating equations (CWGEE). Using simulations, theoretical calculations and real data analysis on periodontal disease, when the cluster size is informative, CWGEE appears to be the recommended choice for marginal parametric inference with clustered longitudinal data that achieves similar parameter estimates and test statistics as WCR while avoiding Monte Carlo computation, while GEE gets biased estimators.
Maison d'édition:
LAP LAMBERT Academic Publishing
Site Web:
https://www.lap-publishing.com/
de (auteur) :
Ming Wang
Numéro de pages:
68
Publié le:
21-05-2010
Stock:
Disponible
Catégorie:
Théorie des probabilités, Stochastique, Statistique Mathématique
Prix:
49.00 €
Mots-clés:
Clustered longitudinal data; Cluster-weighted generalized estimating equations; Generalized estimating equations; Informative cluster size; Within-cluster resampling; Working correlation structures., Clustered longitudinal data; Cluster-weighted generalized estimating equations; Generalized estimating equations; Informative cluster size; Within-cluster resampling; Working correlation structures.

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