Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

The adult ADHD self-report scale for screening for adult attention deficit-hyperactivity disorder (ADHD).

BACKGROUND: Adult attention deficit-hyperactivity disorder (ADHD) is underdiagnosed in the primary care setting despite 3% to 6% of adults having ADHD-like symptoms. The Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1) is a validated, 6-question screen for adult ADHD. Our purpose was to analyze this tool for evaluating patients in a busy primary care setting. METHODS: The ASRS-V1.1 was administered to patients in 8 busy primary care practices. All with a positive score and a random sample of those with a negative screening score were asked to complete the Conners' Adult ADHD Rating Scale Self Report-Short Version. Each was administered within the clinic setting during the same session. Sensitivity, specificity, and positive/negative predictive values were calculated. Data were evaluated for site-specific differences. RESULTS: It took an average of 54.3 seconds (range, 22-252 seconds) to complete the ASRS-V1.1. There was an inconsistency-adjusted sensitivity of 1.0, a specificity of 0.71, a positive predictive value of 0.52, and a negative predictive value of 1.0. No site-specific differences were found. CONCLUSIONS: Because of its ease of use, short time to administer, high sensitivity, and moderate specificity, the ASRS-V1.1 is an effective adult ADHD screening to guide further evaluations for ADHD.