Analysis of prevention program effectiveness with clustered data using generalized estimating equations.
Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleadi...
| Publicado en: | Journal of Consulting & Clinical Psychology Vol. 64; pp. 919 - 927 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
October 1996
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=507522143&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507522143 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0022006X JCC jtl: Journal of Consulting & Clinical Psychology issn: 0022006X maglogo: N pubinfo: dt: October 1996 vid: 64 pid: 34 pub: American Psychological Association artinfo: ui: 507522143 10.1037/0022-006X.64.5.919 ppf: 919 ppct: 8 formats: tig: atl: Analysis of prevention program effectiveness with clustered data using generalized estimating equations. aug: au: Norton, Edward C. Bieler, Gayle S. Ennett, Susan T. su: Regression analysis Substance abuse prevention Psychological techniques Psychology -- Statistical methods sug: subj: Regression analysis Substance abuse prevention Psychological techniques Psychology -- Statistical methods ab: Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluster correlation in regression models with either continuous or binary outcomes. The GEE-independent method yields consistent and robust variance estimates. Data from Project DARE, a youth substance abuse prevention program, are used for illustration. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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