A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

Details

Serval ID
serval:BIB_EF7E66A7C615
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.
Journal
Genetic Epidemiology
Author(s)
Newcombe P.J., Reck B.H., Sun J., Platek G.T., Verzilli C., Kader A.K., Kim S.T., Hsu F.C., Zhang Z., Zheng S.L., Mooser V.E., Condreay L.D., Spraggs C.F., Whittaker J.C., Rittmaster R.S., Xu J.
ISSN
1098-2272 (Electronic)
ISSN-L
0741-0395
Publication state
Published
Issued date
2012
Peer-reviewed
Oui
Volume
36
Number
1
Pages
71-83
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural
Abstract
We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.
Pubmed
Web of science
Create date
10/01/2013 12:23
Last modification date
20/08/2019 17:17
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