Literature DB >> 11130626

Application of a generalized random effects regression model for cluster-correlated longitudinal data to a school-based smoking prevention trial.

A I Sashegyi1, K S Brown, P J Farrell.   

Abstract

In cluster-randomized trials, groups of subjects (clusters) are assigned to treatments, whereas observations are taken on the individual subjects. Since observations on subjects in the same cluster are typically more similar than observations from different clusters, analyses of such data must take intracluster correlation into account rather than assuming independence among all observations. Random effects models are useful for this purpose. The problem becomes more complicated if, in addition, repeated observations are taken on subjects over time. This introduces intraindividual correlation, which is typical for longitudinal studies. The Waterloo Smoking Prevention Project, study 3 (WSPP3), 1989-1996, is a study giving rise to cluster-correlated longitudinal data, where schools were randomized to either a smoking intervention program or to a control condition. Smoking status was assessed on grade 6 students in these schools, with annual follow-up observations throughout elementary and high school years. The authors illustrate the use of a generalized random effects model for analyzing this type of data. This model obtains appropriate estimates and standard errors for both individual-level covariates and those at the level of the cluster.

Mesh:

Year:  2000        PMID: 11130626     DOI: 10.1093/aje/152.12.1192

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  8 in total

1.  The importance and role of intracluster correlations in planning cluster trials.

Authors:  John S Preisser; Beth A Reboussin; Eun-Young Song; Mark Wolfson
Journal:  Epidemiology       Date:  2007-09       Impact factor: 4.822

2.  The effect of implementation climate on program fidelity and student outcomes in autism support classrooms.

Authors:  Hilary E Kratz; Aubyn Stahmer; Ming Xie; Steven C Marcus; Melanie Pellecchia; Jill Locke; Rinad Beidas; David S Mandell
Journal:  J Consult Clin Psychol       Date:  2018-12-20

3.  Randomized Trial of a Computer-Assisted Intervention for Children With Autism in Schools.

Authors:  Melanie Pellecchia; Steven C Marcus; Christine Spaulding; Max Seidman; Ming Xie; Keiran Rump; Erica M Reisinger; David S Mandell
Journal:  J Am Acad Child Adolesc Psychiatry       Date:  2019-04-03       Impact factor: 8.829

4.  Risk of recurrent stillbirth and neonatal mortality: mother-specific random effects analysis using longitudinal panel data from Indonesia (2000 - 2014).

Authors:  Alka Dev
Journal:  BMC Pregnancy Childbirth       Date:  2022-06-28       Impact factor: 3.105

5.  Optimal combination of estimating equations in the analysis of multilevel nested correlated data.

Authors:  J A Stoner; B G Leroux; M Puumala
Journal:  Stat Med       Date:  2010-02-20       Impact factor: 2.373

6.  The role of treatment fidelity on outcomes during a randomized field trial of an autism intervention.

Authors:  David S Mandell; Aubyn C Stahmer; Sujie Shin; Ming Xie; Erica Reisinger; Steven C Marcus
Journal:  Autism       Date:  2013-04-16

Review 7.  School-based programmes for preventing smoking.

Authors:  Roger E Thomas; Julie McLellan; Rafael Perera
Journal:  Cochrane Database Syst Rev       Date:  2013-04-30

8.  Study protocol: implementation of a computer-assisted intervention for autism in schools: a hybrid type II cluster randomized effectiveness-implementation trial.

Authors:  Melanie Pellecchia; Rinad S Beidas; Steven C Marcus; Jessica Fishman; John R Kimberly; Carolyn C Cannuscio; Erica M Reisinger; Keiran Rump; David S Mandell
Journal:  Implement Sci       Date:  2016-11-25       Impact factor: 7.327

  8 in total

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