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Harmonization of Cognitive Measures in Individual Participant Data and Aggregate Data Meta-Analysis
Methods Research Reports
Investigators: Lauren Griffith, PhD, Edwin van den Heuvel, PhD, Isabel Fortier, PhD, Scott Hofer, PhD, Parminder Raina, PhD, Nazmul Sohel, PhD, Hélène Payette, PhD, Christina Wolfson, and Sylvie Belleville, PhD.
Author Information and AffiliationsStructured 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®, 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.
Contents
- Preface
- Acknowledgments
- Key Informants
- Technical Expert Panel
- Report Outline
- Introduction
- Methods and Results: Environmental Scans To Identify Methods of Combining Cognition Data and Statistical Harmonization Methods (Objective 1)
- Methods and Results: Process of Preparing Data for Statistical Harmonization (Objective 2)
- Methods and Results: Implementing and Evaluating Three Methods of Statistical Harmonization Applied to Cognitive Measures (Objective 3)
- Introduction
- Methods of Statistical Harmonization
- Results of Assessing the Cognitive Measures as a Single Construct
- Results of Latent Variable Versus T-Scores and Other Variables
- Methods of Comparison of Statistical Harmonization
- Results of Comparison of Statistical Harmonization
- Results of Sensitivity Analyses
- Discussion
- References
- Glossary of Statistical Terms
- Appendix A Excluded Studies
- Appendix B Variable Description and Categories for the Studies CSHA, CCHS-CLSA, and NuAge
- Appendix C Distribution of Age, Sex, and Education Level for CCHS-CLSA, CSHA, and NuAge
- Appendix D Meta-Analysis Results Weighted Mean Difference Analysis
- Appendix E Additional Regression Results
- Appendix F Summary of Two-Stage IPD Meta-Analysis
- Appendix G Summary of One-Stage IDP Meta-Analysis
Prepared for: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services1, Contract No. 290-2007-10060-I, Prepared by: McMaster University Evidence-based Practice Center, Hamilton, Ontario, Canada
Suggested citation:
Griffith L, van den Heuvel E, Fortier I, Hofer S, Raina P, Sohel N, Payette H, Wolfson C, Belleville S. Harmonization of Cognitive Measures in Individual Participant Data and Aggregate Data Meta-Analysis. Methods Research Report. (Prepared by the McMaster University Evidence-based Practice Center under Contract No. 290-2007-10060-I.) AHRQ Publication No.13-EHC040-EF. Rockville, MD: Agency for Healthcare Research and Quality; March 2013. www.effectivehealthcare.ahrq.gov/reports/final.cfm.
This report is based on research conducted by the McMaster University Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. 290-2007-10060-I). The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
The information in this report is intended to help health care decisionmakers—patients and clinicians, health system leaders, and policymakers, among others—make well-informed decisions and thereby improve the quality of health care services. This report is not intended to be a substitute for the application of clinical judgment. Anyone who makes decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information, i.e., in the context of available resources and circumstances presented by individual patients.
This report may be used, in whole or in part, as the basis for development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied.
None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report.
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