Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology. Export

@article{citeulike:3180548, abstract = {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.}, address = {Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK.}, author = {Burton, P. R. and Hansell, A. L. and Fortier, I. and Manolio, T. A. and Khoury, M. J. and Little, J. and Elliott, P.}, citeulike-article-id = {3180548}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/ije/dyn147}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18676414}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18676414}, doi = {10.1093/ije/dyn147}, issn = {1464-3685}, journal = {International journal of epidemiology}, keywords = {assoc, humgen, lab, mining, stat}, month = {February}, number = {1}, pages = {263--273}, posted-at = {2009-07-01 15:09:38}, priority = {2}, title = {Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology.}, url = {http://dx.doi.org/10.1093/ije/dyn147}, volume = {38}, year = {2009} }

TY - JOUR ID - citeulike:3180548 L3 - citeulike-article-id:3180548 N2 - 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. IS - 1 JF - International journal of epidemiology SN - 1464-3685 AD - Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK. EP - 273 TI - Size matters: just how big is BIG?: Quantifying realistic sample size requirements for human genome epidemiology. VL - 38 SP - 263 KW - assoc KW - humgen KW - lab KW - mining KW - stat AU - Burton, PR AU - Hansell, AL AU - Fortier, I AU - Manolio, TA AU - Khoury, MJ AU - Little, J AU - Elliott, P PY - 2009/02/25/ UR - http://dx.doi.org/10.1093/ije/dyn147 ER -