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.

Cause-specific mortality among Veterans with serious mental illness lost to follow-up.

Objective: Although reduced care engagement has been linked to increased mortality for persons with serious mental illness (SMI), there have been limited investigations into specific mortality causes for this group. This study evaluates the effects of care disengagement on mortality cause and time until death in Veterans with SMI.

Method: A total of 3300 Veterans with SMI lost to Veterans Affairs care for more than 1 year were contacted by providers who attempted treatment reengagement.

Potential impact of adding genetic markers to clinical parameters in predicting prostate biopsy outcomes in men following an initial negative biopsy: findings from the REDUCE trial.

Background: Several germline single nucleotide polymorphisms (SNPs) have been consistently associated with prostate cancer (PCa) risk.

Objective: To determine whether there is an improvement in PCa risk prediction by adding these SNPs to existing predictors of PCa.

Design, Setting, And Participants: Subjects included men in the placebo arm of the randomized Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial in whom germline DNA was available.

Association of the hemochromatosis gene with pazopanib-induced transaminase elevation in renal cell carcinoma.

Background & Aims: Pazopanib has demonstrated clinical benefit in patients with advanced renal cell carcinoma (RCC) and is generally well tolerated. However, transaminase elevations have commonly been observed. This 2-stage study sought to identify genetic determinants of alanine transaminase (ALT) elevations in pazopanib-treated white patients with RCC.

Analysis of alcohol dependence phenotype in the COGA families using covariates to detect linkage.

Linkage analysis methods that incorporate etiological heterogeneity of complex diseases are likely to demonstrate greater power than traditional linkage analysis methods. Several such methods use covariates to discriminate between linked and unlinked pedigrees with respect to a certain disease locus. Here we apply several such methods including two mixture models, ordered subset analysis, and a conditional logistic model to genome scan data on the DSM-IV alcohol dependence phenotype on the Collaborative Studies on Genetics of Alcoholism families, and compare the results to traditional nonparametric linkage analysis.