Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 737-741 |
Journal | European Journal of Epidemiology |
Volume | 24 |
DOIs | |
Publication status | Published - 2009 |
Subject classification (UKÄ)
- Public Health, Global Health, Social Medicine and Epidemiology