IJE Advance Access originally published online on August 1, 2008
International Journal of Epidemiology 2009 38(1):263-273; doi:10.1093/ije/dyn147
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Size matters: just how big is BIG?
Quantifying realistic sample size requirements for human genome epidemiology
1 Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK.
2 Department of Genetics, University of Leicester, Leicester LE1 7RH, UK.
3 Public Population Project in Genomics (P3G), University of Montreal, Canada.
4 Department of Epidemiology and Public Health, Imperial College, London, UK.
5 Dépt de Médecine Sociale et Préventive, University of Montreal, Montreal, Canada.
6 National Human Genome Research Institute, NIH, Bethesda, US.
7 National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, USA.
8 Department of Epidemiology and Community Medicine, University of Ottowa, Ottowa, Canada.
* Corresponding author. Professor of Genetic Epidemiology, Department of Health Sciences, University of Leicester, Adrian Building, Room 217, University Road, Leicester LE1 7RH, UK. E-mail: pb51{at}le.ac.uk
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Abstract |
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Background Despite earlier doubts, a string of recent successes indicates that if sample sizes are large enough, it is possible—both in theory and in practice—to identify and replicate genetic associations with common complex diseases. But human genome epidemiology is expensive and, from a strategic perspective, it is still unclear what ‘large enough’ really means. This question has critical implications for governments, funding agencies, bioscientists and the tax-paying public. Difficult strategic decisions with imposing price tags and important opportunity costs must be taken.
Methods Conventional power calculations for case–control studies disregard many basic elements of analytic complexity—e.g. errors in clinical assessment, and the impact of unmeasured aetiological determinants—and can seriously underestimate true sample size requirements. This article describes, and applies, a rigorous simulation-based approach to power calculation that deals more comprehensively with analytic complexity and has been implemented on the web as ESPRESSO: (www.p3gobservatory.org/powercalculator.htm).
Results Using this approach, the article explores the realistic power profile of stand-alone and nested case–control studies in a variety of settings and provides a robust quantitative foundation for determining the required sample size both of individual biobanks and of large disease-based consortia. Despite universal acknowledgment of the importance of large sample sizes, our results suggest that contemporary initiatives are still, at best, at the lower end of the range of desirable sample size. Insufficient power remains particularly problematic for studies exploring gene–gene or gene–environment interactions.
Discussion Sample size calculation must be both accurate and realistic, and we must continue to strengthen national and international cooperation in the design, conduct, harmonization and integration of studies in human genome epidemiology.
Keywords Human genome epidemiology, biobank, sample size, statistical power, simulation studies, measurement error, reliability, aetiological heterogeneity
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Accepted 8 June 2008
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