ArticleTrials2016
Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application.
Article in Trials, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- 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
- The value of a two-armed Bayesian response adaptive randomization trial.Journal of biopharmaceutical statistics · 2023Article
- Concept and development of an interactive tool for trial recruitment planning and management.Trials · 2021Article
- Bayesian accrual modeling and prediction in multicenter clinical trials with varying center activation times.Pharmaceutical statistics · 2020Article
- Article
- Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies.Clinical trials (London, England) · 2019Article
- Article
- Disparities in Knee and Hip Arthroplasty Outcomes: an Observational Analysis of the ACS-NSQIP Clinical Registry.Journal of racial and ethnic health disparities · 2018Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundSubject recruitment for medical research is challenging. Slow patient accrual leads to increased costs and delays in treatment advances. Researchers need reliable tools to manage and predict the accrual rate. The previously developed Bayesian method integrates researchers' experience on former trials and data from an ongoing study, providing a reliable prediction of accrual rate for clinical studies.
methodsIn this paper, we present a user-friendly graphical user interface program developed in R. A closed-form solution for the total subjects that can be recruited within a fixed time is derived. We also present a built-in Android system using Java for web browsers and mobile devices.
resultsUsing the accrual software, we re-evaluated the Veteran Affairs Cooperative Studies Program 558- ROBOTICS study. The application of the software in monitoring and management of recruitment is illustrated for different stages of the trial.
conclusionsThis developed accrual software provides a more convenient platform for estimation and prediction of the accrual process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.