ArticleJournal of the American Medical Informatics Association : JAMIA2013
Design-phase prediction of potential cancer clinical trial accrual success using a research data mart.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis.Journal of the National Cancer Institute · 2023Pooled it
- Using a Federated Network of Real-World Data to Optimize Clinical Trials Operations.JCO clinical cancer informatics · 2018Review
- Implementation of informatics for integrating biology and the bedside (i2b2) platform as Docker containers.BMC medical informatics and decision making · 2018Article
- Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform.Biomedical informatics insights · 2018Review
- Searching for cures: Inner-city and rural patients' awareness and perceptions of cancer clinical trials.Contemporary clinical trials communications · 2017Article
- SMART-on-FHIR implemented over i2b2.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Mission and Sustainability of Informatics for Integrating Biology and the Bedside (i2b2).EGEMS (Washington, DC) · 2014Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundMany cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled.
objectiveTo assess the value of using a research data mart (RDM) during the design of cancer clinical trials as a predictor of potential patient accrual, so that less trials fail to meet enrollment requirements. MATERIALS AND
methodsThe eligibility criteria for 90 interventional cancer trials were translated into i2b2 RDM queries and cohort sizes obtained for the 2 years prior to the trial initiation. These RDM cohort numbers were compared to the trial accrual requirements, generating predictions of accrual success. These predictions were then compared to the actual accrual performance to evaluate the ability of this methodology to predict the trials' likelihood of enrolling sufficient patients.
resultsOur methodology predicted successful accrual (specificity) with 0.969 (=31/32 trials) accuracy (95% CI 0.908 to 1) and predicted failed accrual (sensitivity) with 0.397 (=23/58 trials) accuracy (95% CI 0.271 to 0.522). The positive predictive value, or precision rate, is 0.958 (=23/24) (95% CI 0.878 to 1). DISCUSSION: A prediction of 'failed accrual' by this methodology is very reliable, whereas a prediction of accrual success is less so, as causes of accrual failure other than an insufficient eligible patient pool are not considered.
conclusionsThe application of this methodology to cancer clinical design would significantly improve cancer clinical research by reducing the costly efforts expended initiating trials that predictably will fail to meet accrual requirements.
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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.