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Quasi-experimental study designs series-paper 10: synthesizing evidence for effects collected from quasi-experimental studies presents surmountable challenges
journal contribution
posted on 2017-09-01, 00:00 authored by B J Becker, A M Aloe, M Duvendack, Tom StanleyTom Stanley, J C Valentine, A Fretheim, P TugwellOBJECTIVE: To outline issues of importance to analytic approaches to the synthesis of quasi-experiments (QEs) and to provide a statistical model for use in analysis. STUDY DESIGN AND SETTING: We drew on studies of statistics, epidemiology, and social-science methodology to outline methods for synthesis of QE studies. The design and conduct of QEs, effect sizes from QEs, and moderator variables for the analysis of those effect sizes were discussed. RESULTS: Biases, confounding, design complexities, and comparisons across designs offer serious challenges to syntheses of QEs. Key components of meta-analyses of QEs were identified, including the aspects of QE study design to be coded and analyzed. Of utmost importance are the design and statistical controls implemented in the QEs. Such controls and any potential sources of bias and confounding must be modeled in analyses, along with aspects of the interventions and populations studied. Because of such controls, effect sizes from QEs are more complex than those from randomized experiments. A statistical meta-regression model that incorporates important features of the QEs under review was presented. CONCLUSION: Meta-analyses of QEs provide particular challenges, but thorough coding of intervention characteristics and study methods, along with careful analysis, should allow for sound inferences.
History
Journal
Journal of clinical epidemiologyVolume
89Pagination
84 - 91Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0895-4356eISSN
1878-5921Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2017, ElsevierUsage metrics
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No categories selectedKeywords
ConfoundingEffect sizeMeta-analysisModerator variablesQuasi-experimentRisk-of-biasHumansMeta-Analysis as TopicModels, StatisticalNon-Randomized Controlled Trials as TopicResearch DesignScience & TechnologyLife Sciences & BiomedicineHealth Care Sciences & ServicesPublic, Environmental & Occupational HealthEFFECT-SIZEMETA-REGRESSIONSYSTEMATIC REVIEWSMETAANALYSISRISKBIASHETEROGENEITYQUALITYISSUES
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