Abstract:
:We present an approach to association studies involving a dozen or so ;response' variables and a few hundred ;explanatory' variables which emphasizes transparency, simplicity, and protection against spurious results. The methods proposed are largely non-parametric, and they are systematically rounded-off by the Benjamini-Hochberg method of multiple testing. An application to the detection of associations between risk factors of heart disease and genetic polymorphisms using the REGRESS dataset provides ample illustration of our approach. Special attention is paid to book-keeping and information-management aspects of data analysis, which allow the creation of an informative and reasonably digestible ;map of relationships'---the end-product of an association study as far as statistics is concerned.
journal_name
Stat Appl Genet Mol Biolauthors
Ferreira JA,Berkhof J,Souverein O,Zwinderman Kdoi
10.2202/1544-6115.1420subject
Has Abstractpub_date
2009-01-01 00:00:00pages
Article 7eissn
2194-6302issn
1544-6115journal_volume
8pub_type
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