A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.
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.
Genetics Division, GlaxoSmithKline, Stevenage, United Kingdom. paul.j.newcombe@gsk.com
Paul J
PJ
Newcombe
Brian H
BH
Reck
Jielin
J
Sun
Greg T
GT
Platek
Claudio
C
Verzilli
A Karim
AK
Kader
Seong-Tae
ST
Kim
Fang-Chi
FC
Hsu
Zheng
Z
Zhang
S Lilly
SL
Zheng
Vincent E
VE
Mooser
Lynn D
LD
Condreay
Colin F
CF
Spraggs
John C
JC
Whittaker
Roger S
RS
Rittmaster
Jianfeng
J
Xu
D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D002138:Calibration; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D011471:Prostatic Neoplasms / Q000235:genetics*; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic; D044465:White People / Q000235:genetics