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Thinking big: large-scale collaborative research in observational epidemiology

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Abstract

Efforts to identify risk factors for chronic diseases have tended to involve observational studies characterised by relatively few disease outcomes. In the absence of individual studies of sufficiently large size, synthesis of available evidence from multiple smaller studies can help enhance statistical power and aid appropriate interpretation. While meta-analyses of published findings can help prioritize research hypotheses, they are inherently limited by the scale of the evidence available for review and by vulnerability to potential reporting biases. By contrast, collaborative analyses of individual participant data from a comprehensive set of relevant epidemiological studies can offer several advantages over moderately sized individual studies or meta-analyses of aggregated data. This review describes those advantages with reference to selected examples.

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Abbreviations

BPC3:

Breast and Prostate Cancer Cohort Consortium

CCGC:

CRP CHD Genetics Collaboration

CHD:

Coronary heart disease

CRP:

C-reactive protein

ENGAGE:

European Network of Genomic and Genetic Epidemiology

EPIC:

European Prospective Investigation into Cancer and Nutrition

ERFC:

Emerging Risk Factors Collaboration

GWAS:

Genomewide association study

HDL:

High-density lipoprotein

IPD:

Individual participant data

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Authors and Affiliations

  1. Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort’s Causeway, Cambridge, CB1 8RN, UK

    Alexander Thompson

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  1. Alexander Thompson
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Correspondence to Alexander Thompson.

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Thompson, A. Thinking big: large-scale collaborative research in observational epidemiology. Eur J Epidemiol 24, 727–731 (2009). https://doi.org/10.1007/s10654-009-9412-1

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  • DOI: https://doi.org/10.1007/s10654-009-9412-1

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