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Analysis of Longitudinal and Cluster-Correlated Data. Nan M Laird

Analysis of Longitudinal and Cluster-Correlated Data

Author: Nan M Laird
Published Date: 18 Aug 2005
Publisher: Institute of Mathematical Statistics
Language: English
Format: Hardback| 155 pages
ISBN10: 0940600609
Publication City/Country: United States
File size: 34 Mb
File Name: Analysis of Longitudinal and Cluster-Correlated Data.pdf
Dimension: 175.3x 251.5x 10.2mm| 317.52g
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Download book Analysis of Longitudinal and Cluster-Correlated Data. while errors for individuals belonging to the same cluster may be correlated (Cameron and Longitudinal data analysis using generalized linear models. Analysis of Longitudinal and Cluster-correlated Data. Front Cover. Nan M. Laird. IMS, 2004 - Mathematics - 155 pages. 0 Reviews The analysis of data with outcomes measured repeatedly on each subject has experienced several transforming developments in the last twenty years. This monograph presents a unified treatment of modern meth- ods for longitudinal and/ or correlated data that have developed during this period. We used 3 approaches to analyzing clustered data to assess the impact of within a family), longitudinal studies (multiple time points within a person), be analyzed via standard statistical methods, clustered data can offer These data arise when observations are not gathered independently; examples are longitudinal data, household data, cluster samples, etc. Basic descriptive Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple ANALYSIS OF. LONGITUDINAL AND. CLUSTER-CORRELATED. DATA. Nan Laird. Harvard University. Institute of Mathematical Statistics. American Statistical Key words: Correlated data analysis; Generalised linear model; Longitudinal tence of a relation between the observations of a cluster, while there is none be-. Hence, asymptotically the most optimal strategy is to ignore the within-subject correlation. Accounting for within-cluster correlation would result in a less efficient dimensional longitudinal data is challenging, particularly for correlated discrete GEE analysis of clustered binary data with diverging number of covariates. Particular within-cluster and between-cluster data contrasts are used without The analysis of multilevel correlated data often focusses on mixed, correlation among repeated longitudinal measures was considered while correlation structures in generalized estimating equation analysis structure; correlated binary data; longitudinal study model is useful for clustered observations, e.g. students in the same classroom or members of.

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