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Harmonization of cognitive measures in individual participant data and aggregate data meta-analysis

Author: Lauren Griffith; United States. Agency for Healthcare Research and Quality,; McMaster University. Evidence-based Practice Center,
Publisher: Rockville, MD : Agency for Healthcare Research and Quality, [2013]
Series: Methods research report.; AHRQ publication, no. 13-EHC040-EF.
Edition/Format:   eBook : Document : National government publication : English
Summary:
OBJECTIVES: The aim of this study was to identify approaches to statistical harmonization which could be used in the context of summary data and/or individual participant data meta-analysis of cognitive measures and to apply and evaluate these different approaches to cognitive measures from three studies. DATA SOURCES: MEDLINE(r), Embase, Web of Science and MathSciNet with a supplemental search using the Google  Read more...
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Genre/Form: Meta-Analysis
Review
Material Type: Document, Government publication, National government publication, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Lauren Griffith; United States. Agency for Healthcare Research and Quality,; McMaster University. Evidence-based Practice Center,
OCLC Number: 847523510
Notes: Title from PDF title page.
"March 2013."
Description: 1 online resource (1 PDF file (various pagings)) : illustrations.
Series Title: Methods research report.; AHRQ publication, no. 13-EHC040-EF.
Responsibility: prepared for Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services ; prepared by McMaster University Evidence-based Practice Center ; investigators, Lauren Griffith, Edwin van den Heuvel, Isabel Fortier, Scott Hofer, Parminder Raina, Nazmul Sohel, Heln̈e Payette, Christina Wolfson, Sylvie Belleville.

Abstract:

OBJECTIVES: The aim of this study was to identify approaches to statistical harmonization which could be used in the context of summary data and/or individual participant data meta-analysis of cognitive measures and to apply and evaluate these different approaches to cognitive measures from three studies. DATA SOURCES: MEDLINE(r), Embase, Web of Science and MathSciNet with a supplemental search using the Google search engine. The references of relevant articles were also checked and a search for more recent articles that cited the articles already identified as being of interest was undertaken. REVIEW METHODS: A two-pronged approach was taken for this environmental scan. First, a search of studies that quantitatively combined data on cognition was conducted. The second component was to identify general literature on statistical methods for data harmonization. Standard environmental scan methods were used to conduct these reviews. The search results were rapidly screened to identify articles of relevance to this review. The references of relevant articles were checked and a search for more recent articles that cited the articles already identified as being of interest was undertaken. RESULTS: Three general classes of statistical harmonization models were identified: (1) standardization methods (e.g., simple linear-, Z-transformations, T-scores, and C-scores); (2) latent variable models; and (3) multiple imputation models. Cross-sectional data from three studies including 9,269 participants were included in the applied analyses to examine the relationship between physical activity and cognition. A harmonization process was undertaken to determine the combinability of data across studies. The latent variable analysis underscored the difficulty harmonizing these cognition data. In general consistency was found among the statistical harmonization methods; however, there was some evidence that heterogeneity can be masked when specific standardization methods were used. CONCLUSIONS: This study provides empirical evidence to inform methods of combining complex constructs using aggregate data (AD) or individual participant data meta-analysis. The results underscore that very careful consideration of inferential equivalence needs to be undertaken prior to combining cognition data across studies. Of the three methods of statistical harmonization for cognition data, T-score standardization is the least desirable compared with the centered score method or latent variable methods. Finally, assessment of the assumptions underlying statistical harmonization is not possible without some individual-level data which are required to assess the potential for bias in combining complex outcomes using AD meta-analysis.

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