Copertina di Clustered longitudinal data analysis
Titolo del libro:

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
Lingua del libro:
Inglese
Risvolto di copertina:
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.
Casa editrice:
LAP LAMBERT Academic Publishing
Sito Web:
https://www.lap-publishing.com/
Da (autore):
Ming Wang
Numero di pagine:
68
Pubblicato il:
21.05.2010
Giacenza di magazzino:
Disponibile
categoria:
Teoria della probabilità, processi stocastici, statistica matematica
Prezzo:
323,89 R$
Parole chiave:
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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