ArticleClinical trials (London, England)2019
Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies.
Article in Clinical trials (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Freely accessible software for recruitment prediction and recruitment monitoring of clinical trials: A systematic review.Contemporary clinical trials communications · 2024Review
- Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.Contemporary clinical trials communications · 2024Article
- Bayesian accrual modeling and prediction in multicenter clinical trials with varying center activation times.Pharmaceutical statistics · 2020Article
- Article
- Improving the efficiency of clinical trials by standardizing processes for Investigator Initiated Trials.Contemporary clinical trials communications · 2020Article
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6 authors.
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Abstract
backgroundMonitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise.
methodsThis article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application.
resultsFirst released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center.
conclusionThe Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.
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