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Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center
INTRODUCTION: As cancer center funds are allocated toward several resources, clinical trial offices and the clinical trial infrastructure is constantly scrutinized. It has been shown that 20% of clinical trials fail to achieve their accrual goal and in an institutional level several trials are open...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658414/ https://www.ncbi.nlm.nih.gov/pubmed/31372575 http://dx.doi.org/10.1016/j.conctc.2019.100421 |
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author | Iruku, Praveena Goros, Martin Gelfond, Jonathan Chang, Jenny Padalecki, Susan Mesa, Ruben Kaklamani, Virginia G. |
author_facet | Iruku, Praveena Goros, Martin Gelfond, Jonathan Chang, Jenny Padalecki, Susan Mesa, Ruben Kaklamani, Virginia G. |
author_sort | Iruku, Praveena |
collection | PubMed |
description | INTRODUCTION: As cancer center funds are allocated toward several resources, clinical trial offices and the clinical trial infrastructure is constantly scrutinized. It has been shown that 20% of clinical trials fail to achieve their accrual goal and in an institutional level several trials are open with poor accrual. We sought to identify factors that are associated with clinical trial accrual and develop a model to predict clinical trial accrual METHODS AND MATERIAL: We identified all clinical trials from 1999 to 2015 at UT Health Cancer Center San Antonio. We included observational as well as interventional clinical trials. We collected several variables such as type of study, type of malignancy, trial phase, PI of study. RESULTS: In total we included 297 clinical trials. We identified several factors to be associated with clinical trial accrual (Sponsor type, trial phase, disease category, type of trial, disease state and whether the trial involved a new investigational agent). We developed a predictive model with an AUC of 0.65 that showed that observational, interventional, industry-sponsored trials and trials authored by the local PI were more likely to achieve their accrual goal. CONCLUSION: We were able to identify several factors that were significantly associated with clinical trial accrual. Based on these factors we developed a prediction model for clinical trial accrual. We believe that use of this model can help improve our cancer centers clinical trial portfolio and help in fund allocation. |
format | Online Article Text |
id | pubmed-6658414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-66584142019-08-01 Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center Iruku, Praveena Goros, Martin Gelfond, Jonathan Chang, Jenny Padalecki, Susan Mesa, Ruben Kaklamani, Virginia G. Contemp Clin Trials Commun Article INTRODUCTION: As cancer center funds are allocated toward several resources, clinical trial offices and the clinical trial infrastructure is constantly scrutinized. It has been shown that 20% of clinical trials fail to achieve their accrual goal and in an institutional level several trials are open with poor accrual. We sought to identify factors that are associated with clinical trial accrual and develop a model to predict clinical trial accrual METHODS AND MATERIAL: We identified all clinical trials from 1999 to 2015 at UT Health Cancer Center San Antonio. We included observational as well as interventional clinical trials. We collected several variables such as type of study, type of malignancy, trial phase, PI of study. RESULTS: In total we included 297 clinical trials. We identified several factors to be associated with clinical trial accrual (Sponsor type, trial phase, disease category, type of trial, disease state and whether the trial involved a new investigational agent). We developed a predictive model with an AUC of 0.65 that showed that observational, interventional, industry-sponsored trials and trials authored by the local PI were more likely to achieve their accrual goal. CONCLUSION: We were able to identify several factors that were significantly associated with clinical trial accrual. Based on these factors we developed a prediction model for clinical trial accrual. We believe that use of this model can help improve our cancer centers clinical trial portfolio and help in fund allocation. Elsevier 2019-07-19 /pmc/articles/PMC6658414/ /pubmed/31372575 http://dx.doi.org/10.1016/j.conctc.2019.100421 Text en © 2019 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Iruku, Praveena Goros, Martin Gelfond, Jonathan Chang, Jenny Padalecki, Susan Mesa, Ruben Kaklamani, Virginia G. Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title | Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title_full | Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title_fullStr | Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title_full_unstemmed | Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title_short | Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center |
title_sort | developing a model to predict accrual to cancer clinical trials: data from an nci designated cancer center |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658414/ https://www.ncbi.nlm.nih.gov/pubmed/31372575 http://dx.doi.org/10.1016/j.conctc.2019.100421 |
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