This work was partially supported by the National Institutes of Health (CA140262 and RC2 CA148463 to J.X.).
A Comparison of Bayesian and Frequentist Approaches to Incorporating External Information for the Prediction of Prostate Cancer Risk
Article first published online: 29 DEC 2011
© 2011 Wiley Periodicals, Inc.
Volume 36, Issue 1, pages 71–83, January 2012
How to Cite
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. and Xu, J. (2012), A Comparison of Bayesian and Frequentist Approaches to Incorporating External Information for the Prediction of Prostate Cancer Risk. Genet. Epidemiol., 36: 71–83. doi: 10.1002/gepi.21600
- Issue published online: 29 DEC 2011
- Article first published online: 29 DEC 2011
- Manuscript Accepted: 28 OCT 2011
- Manuscript Revised: 12 OCT 2011
- Manuscript Received: 21 JUL 2011
- National Institutes of Health. Grant Numbers: CA140262, CA148463
- prostate cancer;
- genetic clinical risk prediction;
- genetic scores;
- Bayesian logistic regression;
- predictive assessment
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.