Abstract: PSA measurements are used to assess the risk for prostate cancer. PSA range and PSA kinetics such as PSA velocity have been correlated with in creased cancer detection and assist the clinician in deciding when prostate biopsy should be performed. Our aim is to evaluate the use of a novel, maxi mum likelihood estimation - prostate specific antigen (MLE-PSA) model for predicting the probability of prostate cancer using serial PSA measurements combined with PSA velocity in order to assess whether this reduces the need for prostate biopsy. A total of 1976 Caucasian patients were included. All these patients had at least 6 PSA serial measurements; all underwent trans-rectal biopsy with minimum 12 cores within the past 10 years. A multivariate logistic re gression model was developed using maximum likelihood estimation (MLE) based on the following parameters (age, at least 6 PSA serial measurements, baseline median natural logarithm of the PSA (ln(PSA)) and PSA velocity (ln(PSAV)), baseline process capability standard deviation of ln(PSA) and ln(PSAV), significant special causes of variation in ln(PSA) and ln(PSAV) detected using control chart logic, and the volatility of the ln(PSAV). We then compared prostate cancer probability using MLE-PSA to the results of prostate needle biopsy. The MLE-PSA model with a 50% cut-off probability has a sensitivity of 87%, specificity of 85%, positive predictive value (PPV) of 89%, and negative predictive value (NPV) of 82%. By contrast, a single PSA value with a 4ng/ml threshold has a sensitivity of 59%, specificity of 33%, PPV of 56%, and NPV of 36% using the same population of patients used to generate the MLE-PSA model. Based on serial PSA measurements, the use of the MLE-PSA model significantly (p-value < 0.0001) improves prostate cancer detection and reduces the need for prostate biopsy.
Abstract: Childhood obesity is a major health concern. The associated health risks dramatically reduce lifespan and increase healthcare costs. The goal was to develop methodology to identify as early in life as possible whether or not a child would become obese at age five. This diagnostic tool would facilitate clinical monitoring to prevent and or minimize obesity. Obesity is measured by Body Mass Index (BMI), but an improved metric, the ratio of weight to height (or length) (WOH), is proposed from this re search for detecting early obesity. Results of this research demonstrate that WOH performs better than BMI for early detection of obesity in individuals using a longitudinal decision analysis (LDA), which is essentially an indi viduals type control chart analysis about a trend line. Utilizing LDA, the odds of obesity of a child at age five is indicated before the second birth day with 95% sensitivity and 97% specificity. Further, obesity at age five is indicated with 75% specificity before two months and with 84% specificity before three months of age. These results warrant expanding this study to larger cohorts of normal, overweight, and obese children at age five from different healthcare facilities to test the applicability of this novel diagnostic tool.
Abstract: We have developed a tool for model space exploration and variable selec tion in linear regression models based on a simple spike and slab model (Dey, 2012). The model chosen is the best model with minimum final prediction error (FPE) values among all other models. This is implemented via the R package modelSampler. However, model selection based on FPE criteria is dubious and question able as FPE criteria can be sensitive to perturbations in the data. This R package can be used for empirical assessment of the stability of FPE criteria. A stable model selection is accomplished by using a bootstrap wrapper that calls the primary function of the package several times on the bootstrapped data. The heart of the method is the notion of model averaging for sta ble variable selection and to study the behavior of variables over the entire model space, a concept invaluable in high dimensional situations.
Abstract: We have developed an enhanced spike and slab model for variable selection in linear regression models via restricted final prediction error (FPE) criteria; classic examples of which are AIC and BIC. Based on our proposed Bayesian hierarchical model, a Gibbs sampler is developed to sample models. The special structure of the prior enforces a unique mapping between sampling a model and calculating constrained ordinary least squares estimates for that model, which helps to formulate the restricted FPE criteria. Empirical comparisons are done to the lasso, adaptive lasso and relaxed lasso; followed by a real life data example.