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Meta-Analysis and Subgroups
by
Higgins, Julian P. T.
, Borenstein, Michael
in
Analysis
/ Child and School Psychology
/ Classrooms
/ Data Interpretation, Statistical
/ Group analysis
/ Health Psychology
/ Humans
/ Intervention
/ Interventionism
/ Medical treatment
/ Medicine
/ Medicine & Public Health
/ Meta-analysis
/ Models, Theoretical
/ Obesity
/ Obesity, Morbid - prevention & control
/ Prevention
/ Prevention programs
/ Public Health
/ Statistical models
/ Statistical power
/ Statistics
/ Systematic review
/ Teachers
/ Variants
2013
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Meta-Analysis and Subgroups
by
Higgins, Julian P. T.
, Borenstein, Michael
in
Analysis
/ Child and School Psychology
/ Classrooms
/ Data Interpretation, Statistical
/ Group analysis
/ Health Psychology
/ Humans
/ Intervention
/ Interventionism
/ Medical treatment
/ Medicine
/ Medicine & Public Health
/ Meta-analysis
/ Models, Theoretical
/ Obesity
/ Obesity, Morbid - prevention & control
/ Prevention
/ Prevention programs
/ Public Health
/ Statistical models
/ Statistical power
/ Statistics
/ Systematic review
/ Teachers
/ Variants
2013
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Do you wish to request the book?
Meta-Analysis and Subgroups
by
Higgins, Julian P. T.
, Borenstein, Michael
in
Analysis
/ Child and School Psychology
/ Classrooms
/ Data Interpretation, Statistical
/ Group analysis
/ Health Psychology
/ Humans
/ Intervention
/ Interventionism
/ Medical treatment
/ Medicine
/ Medicine & Public Health
/ Meta-analysis
/ Models, Theoretical
/ Obesity
/ Obesity, Morbid - prevention & control
/ Prevention
/ Prevention programs
/ Public Health
/ Statistical models
/ Statistical power
/ Statistics
/ Systematic review
/ Teachers
/ Variants
2013
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Journal Article
Meta-Analysis and Subgroups
2013
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Overview
Subgroup analysis is the process of comparing a treatment effect for two or more variants of an intervention—to ask, for example, if an intervention’s impact is affected by the setting (school versus community), by the delivery agent (outside facilitator versus regular classroom teacher), by the quality of delivery, or if the long-term effect differs from the short-term effect. While large-scale studies often employ subgroup analyses, these analyses cannot generally be performed for small-scale studies, since these typically include a homogeneous population and only one variant of the intervention. This limitation can be bypassed by using meta-analysis. Meta-analysis allows the researcher to compare the treatment effect in different subgroups, even if these subgroups appear in separate studies. We discuss several statistical issues related to this procedure, including the selection of a statistical model and statistical power for the comparison. To illustrate these points, we use the example of a meta-analysis of obesity prevention.
Publisher
Springer US,Springer Nature B.V
Subject
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