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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-09-01
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| Series: | Contemporary Clinical Trials Communications |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451865419300304 |
| _version_ | 1819069431102308352 |
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| author | Praveena Iruku Martin Goros Jonathan Gelfond Jenny Chang Susan Padalecki Ruben Mesa Virginia G. Kaklamani |
| author_facet | Praveena Iruku Martin Goros Jonathan Gelfond Jenny Chang Susan Padalecki Ruben Mesa Virginia G. Kaklamani |
| author_sort | Praveena Iruku |
| collection | DOAJ |
| 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. Keywords: Clinical trials, Prediction, Model, Accrual, Cancer center |
| first_indexed | 2024-12-21T16:49:56Z |
| format | Article |
| id | doaj.art-f067b0cd91b64858b03cf0600e01e0d3 |
| institution | Directory Open Access Journal |
| issn | 2451-8654 |
| language | English |
| last_indexed | 2024-12-21T16:49:56Z |
| publishDate | 2019-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Contemporary Clinical Trials Communications |
| spelling | doaj.art-f067b0cd91b64858b03cf0600e01e0d32022-12-21T18:56:54ZengElsevierContemporary Clinical Trials Communications2451-86542019-09-0115Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer centerPraveena Iruku0Martin Goros1Jonathan Gelfond2Jenny Chang3Susan Padalecki4Ruben Mesa5Virginia G. Kaklamani6Department of Hematology/Oncology, University of Colorado Health, Colorado Springs, CO, USADepartment of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, TX, USADepartment of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, TX, USAHouston Methodist Cancer Center, Houston, TX, USADepartment of Medicine, UT Health Cancer Center San Antonio, San Antonio, TX, USADepartment of Medicine, UT Health Cancer Center San Antonio, San Antonio, TX, USADepartment of Medicine, UT Health Cancer Center San Antonio, San Antonio, TX, USA; Corresponding author. University of Texas Health Science Center San Antonio 7979 Wurzbach Road San Antonio, TX, 78229, USA.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. Keywords: Clinical trials, Prediction, Model, Accrual, Cancer centerhttp://www.sciencedirect.com/science/article/pii/S2451865419300304 |
| spellingShingle | Praveena Iruku Martin Goros Jonathan Gelfond Jenny Chang Susan Padalecki Ruben Mesa Virginia G. Kaklamani Developing a model to predict accrual to cancer clinical trials: Data from an NCI designated cancer center Contemporary Clinical Trials Communications |
| 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 |
| url | http://www.sciencedirect.com/science/article/pii/S2451865419300304 |
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