PURPOSE: Clinical trials often fail to reach their anticipated end points, most frequently because of poor accrual. Prior studies have analyzed trial termination, but it has not been easy to assess accrual estimates using international databases such as ClinicalTrials.gov because of limitations in accessing accrual information. Specifically, it is not easy to extract both anticipated and actual accrual of clinical trials. We designed a new algorithmic approach to extracting trial accrual data from ClinicalTrials.gov and used it to estimate the sufficiency of patient accrual onto genitourinary (GU) cancer trials. METHODS: We queried ClinicalTrials.gov for completed/terminated phase II and III clinical trials for prostate, bladder, kidney, testicular, and ureteral cancers registered after 2007. We extracted trial characteristics from available XML files. We then used a Python algorithm to access prior trial registrations on the ClinicalTrials.gov archive site and extract both anticipated and actual accrual numbers. We then compared the actual accrual of each trial to its anticipated accrual and defined sufficient accrual as 85% of anticipated accrual. RESULTS: The algorithm was 100% accurate compared with hand extraction in a small validation subset. A total of 925 trials were included, of which 840 (91%) had both anticipated and actual accrual. Only 418 (50%) trials had sufficient accrual (≥ 85% of anticipated). Considering only trials marked as successfully completed, 395/597 (66%) reached sufficient accrual. CONCLUSION: GU cancer trials often do not meet their anticipated accrual goals. New approaches to trial conduct are direly needed. Our reproducible and scalable approach to extracting accrual information can be applied to analysis of ClinicalTrials.gov in future analyses in the hope of improving the efficiency of the clinical trials enterprise.
PURPOSE: Clinical trials often fail to reach their anticipated end points, most frequently because of poor accrual. Prior studies have analyzed trial termination, but it has not been easy to assess accrual estimates using international databases such as ClinicalTrials.gov because of limitations in accessing accrual information. Specifically, it is not easy to extract both anticipated and actual accrual of clinical trials. We designed a new algorithmic approach to extracting trial accrual data from ClinicalTrials.gov and used it to estimate the sufficiency of patient accrual onto genitourinary (GU) cancer trials. METHODS: We queried ClinicalTrials.gov for completed/terminated phase II and III clinical trials for prostate, bladder, kidney, testicular, and ureteral cancers registered after 2007. We extracted trial characteristics from available XML files. We then used a Python algorithm to access prior trial registrations on the ClinicalTrials.gov archive site and extract both anticipated and actual accrual numbers. We then compared the actual accrual of each trial to its anticipated accrual and defined sufficient accrual as 85% of anticipated accrual. RESULTS: The algorithm was 100% accurate compared with hand extraction in a small validation subset. A total of 925 trials were included, of which 840 (91%) had both anticipated and actual accrual. Only 418 (50%) trials had sufficient accrual (≥ 85% of anticipated). Considering only trials marked as successfully completed, 395/597 (66%) reached sufficient accrual. CONCLUSION: GU cancer trials often do not meet their anticipated accrual goals. New approaches to trial conduct are direly needed. Our reproducible and scalable approach to extracting accrual information can be applied to analysis of ClinicalTrials.gov in future analyses in the hope of improving the efficiency of the clinical trials enterprise.
Authors: Kristian D Stensland; Samuel D Kaffenberger; Arvin K George; Todd M Morgan; David C Miller; Simpa S Salami; Rodney L Dunn; Ganesh S Palapattu; Jeffrey S Montgomery; Brent K Hollenbeck; Ted A Skolarus Journal: Contemp Clin Trials Date: 2021-10-19 Impact factor: 2.226
Authors: Stuart G Nicholls; Kelly Carroll; Spencer Phillips Hey; Merrick Zwarenstein; Jennifer Zhe Zhang; Hayden P Nix; Jamie C Brehaut; Joanne E McKenzie; Steve McDonald; Charles Weijer; Dean A Fergusson; Monica Taljaard Journal: J Clin Epidemiol Date: 2021-03-28 Impact factor: 6.437
Authors: Kristian D Stensland; Krystal DePorto; James Ryan; Samuel Kaffenberger; Lael S Reinstatler; Matthew Galsky; David Canes; Ted A Skolarus; Alireza Moinzadeh Journal: Urol Oncol Date: 2020-11-27 Impact factor: 3.498