Do African-American men need separate prostate cancer screening guidelines?

BACKGROUND: In 2012, the United States Preventative Services Task Force issued new guidelines recommending that male U.S. residents, irrespective of race, no longer be screened for prostate cancer. In African American men, the incidence of prostate cancer is almost 60 % higher and the mortality rate is two to three times greater than in Caucasians. The purpose of this study is to reduce African American men's prostate cancer burden by demonstrating they need separate screening guidelines. METHODS: We performed a PubMed search using the keywords: African American, Prostate cancer, Outcomes, Molecular markers, Prostate-specific Antigen velocity, PSA density, and to derive data relevant to our hypothesis. RESULTS: In our literature review, we identified several aspects of prostate cancer that are different in Caucasian and African American men. These included prostate cancer incidence and outcome, the clinical course of the disease, serum PSA levels, genetic differences, and social barriers. It's also important to note that the USPSTF guidelines were based on two studies, one of which reported that only 4 % of its participants were African American. The other did not report demographic information, but used participants from seven European countries with small African American populations. CONCLUSION: Given the above, we conclude that separate prostate cancer screening guidelines are greatly necessary to help save the lives of African Americans

Using data mining to predict success in a weight loss trial

Background: Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Methods: Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). Results: The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m-2) were more likely to be successful than those with a BMI between 25 and 27 kg m-2. Conclusions: Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results