Am J Epidemiol 2002; 156:204-210.
Copyright © 2002 by the
Johns Hopkins Bloomberg School of Public Health
SPECIAL ARTICLE |
Commentary: Meta-analysis of Individual Participants’ Data in Genetic Epidemiology
1 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, and Ioannina Biomedical Research Institute, Foundation for Research and Technology–Hellas, Ioannina, Greece.
2 Department of Medicine, Tufts University School of Medicine, Boston, MA.
3 Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD.
4 Viral Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD.
Received for publication October 9, 2001; accepted for publication March 14, 2002.
bias, epidemiology; genetics; meta-analysis
Abbreviations: Abbreviations: AIDS, acquired immunodeficiency syndrome; CCR2, C-C chemokine receptor 2; CCR5, C-C chemokine receptor 5; CDC, Centers for Disease Control and Prevention; D32, 32-base pair deletion; HIV-1, human immunodeficiency virus type 1; MIPD, meta-analysis of individual participants’ data; MPL, meta-analysis of the published literature; SDF-1, stromal cell-derived factor 1.
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INTRODUCTION |
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With more than 30,000 human genes and several genetic markers per gene, the potential for identifying genetic associations for various diseases is enormous (1–5). Genetic effects are often modest: Many subjects must be studied, and single epidemiologic studies are unlikely to be definitive. Meta-analysis may help meet the challenge of synthesizing data from studies of genetic epidemiology (6). Several meta-analyses of published literature (MPL) have already appeared in the field (7). Meta-analysis of epidemiologic studies (8) is controversial. Critics have focused on variability in study designs, poor data quality, insufficient confounder adjustment, publication bias, and spuriously narrow confidence intervals (9–12). Use of the raw information from individual subjects instead of published data avoids some of these problems. Meta-analysis of individual patient data has been applied successfully in randomized trials (13, 14). The equivalent
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CONDUCT OF THE MPL |
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CONDUCT OF THE MIPD |
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ADVANTAGES OF MIPD (table 1) |
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Data
Completeness of information
Standardization of information
Analysis
Time-to-event analyses
Adjusted/multivariate analyses
Linkage disequilibrium
Alternative genetic models and effects of multiple genes
Population subgroups
Interpretation
Heterogeneity
Assessing sampling bias
Other
Establishing collaborations
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DISADVANTAGES OF MIPD (table 1) |
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Data
Interpretation
Resources
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FINAL COMMENT |
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