The quality by design (QbD) framework holds promise for improving success rates for completion of clinical studies, which often fail to complete on time. Initially used in manufacturing, the framework is now frequently applied to clinical trials to anticipate risks and avoid challenges that impact study completion or the credibility of results. The Southern California Clinical and Translational Science Institute created and implemented a program based on QbD to increase the success of research studies, including clinical trials and other study designs, being conducted by scholars in our Mentored Career Development Program and awardees in our Pilot Grant Programs. The program’s three components are QbD Design Studios, project management, and team science support. The overall goal is to increase study quality and efficiency, thereby improving study completion success rates and, ultimately, driving innovations in healthcare and public health. The current article describes QbD program elements in detail, along with preliminary results from initial implementation, approaches for evaluating the program’s implementation and impact on study success, and plans to disseminate the program widely.

A long-standing classification problem in archaeology is determining the type of weapon delivery system used by people in the past. This is usually done by comparing archaeological points to known dart and arrow points from the ethnographic and archaeological record. There are no simple criteria to discriminate between these two states and the challenge is to identify a subset of traits and their interactions to solve this problem. Here we introduce a Bayesian technique of classifying dart and arrow. Using machine-learning feature selection, we first find the optimal set of variables for classification. We then use a Generalized Additive Model to model the interaction of these variables in a Bayesian logistic framework to capture the nonlinear decision boundary between darts and arrows and assign probabilities of a point belonging to either state. To counteract the imbalance of having more arrows than darts, we adjust the typical decision cutoff using an iterative approach that balances sensitivity and specificity. We increase the sample of known arrow and dart points with 102 previously published specimens from the West. The code for our model is available and easily accessible through an online application. We apply our model to published dart-versus-arrow classifications to demonstrate its utility.

This study investigates screening practices for antimicrobial-resistant organisms (AROs) in seventy-five hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP). Screening practices varied with widespread MRSA screening, selective carbapenemase-producing organisms (CPO) screening, and limited vancomycin-resistant Enterococcus (VRE) screening. These findings may help interpret ARO rates within CNISP hospitals and inform screening practices.

Objectives/Goals: To identify clinical trial teams that are at risk of not meeting their recruitment goals as early in the recruitment period as possible, this project aims to provide timely accrual information and projected forecasts for accruals by the end of the recruitment period across all trials at USC. Methods/Study Population: This project aggregates recruitment accrual data periodically from OnCore to create per-study accrual pages that contain an up-to-date accrual chart, metrics like expected and actual accrual per month, and projected recruitment based on an X-month moving average (3 months by default). Trials at risk are identified as early as possible by using these projections to classify risk. In this initial phase, we’ve classified trials as medium risk (80%–99% accrual) or high risk (less than 80% accrual). The dashboard is currently available for all clinical trials at USC and users are automatically restricted to the studies that they administer or work on depending on their role. Results/Anticipated Results: The dashboard will provide visibility across the institution for the current accrual for all clinical trials in a standard, user-friendly format and use the same metrics and definitions of risk for trial accruals not meeting their targets. This will allow the institution to identify trials that need intervention to get back on track using a single set of criteria across all research teams. Users in different roles, whether department heads, principal investigators, or study coordinators can view the current accrual for all the trials that they administer or work on in one central location. The dashboard will also help to identify quality issues in OnCore by performing data quality checks nightly. Discussion/Significance of Impact: By providing a central location for role-based access to timely clinical trial accrual for the institution, the dashboard helps to identify trials at risk of not meeting their recruitment targets as early as possible to provide corrective advice/measures.