Profiling medical sites based on adverse events data for multi-center clinical trials

Profiling medical sites is an important activity in both clinical research and practice. Many organizations provide public report cards comparing outcomes across hospitals. An analogous concept applied in multicenter clinical trials, such “report cards” guide sponsors to choose sites while designing a study, help identify areas of improvement for sites, and motivate sites to perform better. Sponsors include comparative performance of sites, a concept to perform risk-based monitoring and central statistical monitoring. In clinical research, report cards are powerful tools for relating site performance to treatment benefits. This study evaluates approaches to estimating the proportion of adverse events at the site-level in a multicenter clinical trial setting and also methods in detecting outlying sites. We address three topics. First we assess the performance of different models for obtaining estimates of adverse events rates utilizing Bayesian beta-binomial and binomial logit-normal models with MCMC estimation and fixed effects maximum likelihood estimation (MLE) methods. We - vary sample sizes, number of medical sites, overall adverse event rates, and intraclass correlation within sites. Second, we compare the performance of these methods in identifying outlier sites, contrasting MLE and Bayesian approaches. A fixed threshold method detects sites as outliers under a Bayesian approach, while in the fixed effects assumption, a 95% interval-based approach is applied. Third, we extend this approach in estimating multiple outcomes at the site level and detecting outlier sites. A standard bivariate normal MLE method is compared to a Bayesian bivariate binomial logit-normal MCMC. These are examined using simulation studies. Results show for single outcomes, Bayesian beta-binomial MCMC method perform well under certain parametric conditions for estimation and detecting outlier sites. For multiple outcomes with higher adverse event rate and larger difference between outliers and non-outliers, for detecting outlier sites, both methods – Bayesian MCMC and MLE work well, irrespective of the correlation between outcomes.2020-02-14T00:00:00

Big Data Analysis and Programming for Engineers

This text serves to cover critical programming, data analysis, statistical analysis, and mathematical skills for engineers. In particular fundamental programming skills are demonstrated using Mathematica specifically the importing of data sets, loop structures, plotting and statistically analyzing data, image analysis, and machine learning. Critical engineering topics such as solid mechanics, vibrations, and engineering problems which require solving ODEs and PDEs are covered.https://scholarlycommons.pacific.edu/open-textbooks/1026/thumbnail.jp

Analysis of static contact in laminated composite plates using damage mechanics

This document reports the research performed to construct a model capable of predicting the damage development caused by out-of-plane static loading in laminated graphite/epoxy composite plates