  This manuscript proposes a novel empirical Bayes technique for regularizing
regression coefficients in predictive models. When predictions from a
previously published model are available, this empirical Bayes method provides
a natural mathematical framework for shrinking coefficients toward the
estimates implied by the body of existing research rather than the shrinkage
toward zero provided by traditional L1 and L2 penalization schemes. The method
is applied to two different prediction problems. The first involves the
construction of a model for predicting whether a single nucleotide polymorphism
(SNP) of the KCNQ1 gene will result in dysfunction of the corresponding voltage
gated ion channel. The second involves the prediction of preoperative serum
creatinine change in patients undergoing cardiac surgery.
