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.

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. Fisher's Exact Tests evaluated associations between mortality cause and reengagement status, and a Cox proportional hazard model evaluated the association between reengagement and survival. RESULTS: During the study, 146 (4.6%) patients died. A lack of reengagement was associated with increased noninjury death [odds ratio (OR)=1.64], increased cancer-based mortality (OR=4.76) and an average of 97.4 fewer days of life. CONCLUSIONS: Care reengagement may support medical care management and reduce preventable medical mortality for Veterans with SMI.

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. All men had an initial negative prostate biopsy and underwent study-mandated biopsies at 2 yr and 4 yr. Predictive performance of baseline clinical parameters and/or a genetic score based on 33 established PCa risk-associated SNPs was evaluated. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Area under the receiver operating characteristic curves (AUC) were used to compare different models with different predictors. Net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess changes in risk prediction by adding genetic markers. RESULTS AND LIMITATIONS: Among 1654 men, genetic score was a significant predictor of positive biopsy, even after adjusting for known clinical variables and family history (p = 3.41 × 10(-8)). The AUC for the genetic score exceeded that of any other PCa predictor at 0.59. Adding the genetic score to the best clinical model improved the AUC from 0.62 to 0.66 (p<0.001), reclassified PCa risk in 33% of men (NRI: 0.10; p=0.002), resulted in higher net benefit from DCA, and decreased the number of biopsies needed to detect the same number of PCa instances. The benefit of adding the genetic score was greatest among men at intermediate risk (25th percentile to 75th percentile). Similar results were found for high-grade (Gleason score ≥ 7) PCa. A major limitation of this study was its focus on white patients only. CONCLUSIONS: Adding genetic markers to current clinical parameters may improve PCa risk prediction. The improvement is modest but may be helpful for better determining the need for repeat prostate biopsy. The clinical impact of these results requires further study.

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. METHODS: Data from two separate clinical studies were used to examine the association of genetic polymorphisms with maximum on-treatment ALT levels. RESULTS: Of 6852 polymorphisms in 282 candidate genes examined in an exploratory dataset of 115 patients, 92 polymorphisms in 40 genes were significantly associated with ALT elevation (p<0.01). Two markers (rs2858996 and rs707889) in the HFE gene, which are not yet known to be associated with hemochromatosis, showed evidence for replication. Because of multiple comparisons, there was a 12% likelihood the replication occurred by chance. These two markers demonstrated strong linkage disequilibrium (r(2)=0.99). In the combined dataset, median (25-75th percentile) maximum ALT values were 1.2 (0.7-1.9), 1.1 (0.8-2.5), and 5.4 (1.9-7.6)×ULN for rs2858996 GG (n=148), GT (n=82), and TT (n=1 2) genotypes, respectively. All 12 TT patients had a maximum ALT>ULN, and 8 (67%) had ALT≥3×ULN. The odds ratio (95% CI) for ALT≥3×ULN for TT genotype was 39.7 (2.2-703.7) compared with other genotypes. As a predictor of ALT≥3×ULN, the TT genotype had a negative predictive value of 0.83 and positive predictive value of 0.67. No TT patients developed liver failure. CONCLUSIONS: The rs2858996/rs707889 polymorphisms in the HFE gene may be associated with reversible ALT elevation in pazo-panib-treated 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. In general, there was little agreement among the various covariate-based linkage statistics. Linkage signals with empirical p-values less than 0.001 were detected on chromosomes 3, 4, 7, 10, and 12, with the highest peak occurring at the GABRB1 gene using the ecb21 covariate.