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Selective inference in complex research
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DOI：10.1098/rsta.2009.0127.png)
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Abstract
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We explain the problem of selective inference in complex research using a recently published study: a replicability study of the associations in order to reveal and establish risk loci for type 2 diabetes. The false discovery rate approach to such problems will be reviewed, and we further address two problems: (i) setting confidence intervals on the size of the risk at the selected locations and (ii) selecting the replicable results.
Keywords:
false discovery rate
false coverage rate
multiple comparisons
replicability
genome-wise association scan
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