Empirical Bayes and semi-Bayes adjustments for a vast number of estimations.
Investigators in modern molecular/genetic epidemiology studies commonly analyze data on a vast number of candidate genetic markers. In such situations, rather than conventional estimation of effects (odds ratios), more accurate estimation methods are needed. The author proposes consideration of empirical Bayes and semi-Bayes methods, which yield 'adjustments for multiple estimations' by shrinking conventional effect estimates towards the overall average effect.
Strömberg
Ulf
U
Department of Occupational and Environmental Medicine, Lund University, Lund, Sweden. ulf.stromberg@med.lu.se
D001499:Bayes Theorem; D016021:Epidemiologic Studies; D005787:Gene Frequency; D005819:Genetic Markers; D016678:Genome; D055106:Genome-Wide Association Study; D006801:Humans; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide
D005819:Genetic Markers