Literature DB >> 11786585

Factors affecting workload of cancer clinical trials: results of a multicenter study of the National Cancer Institute of Canada Clinical Trials Group.

Kathyrn Roche1, Nancy Paul, Bobbi Smuck, Marlo Whitehead, Benny Zee, Joseph Pater, Mary-Anne Hiatt, Hugh Walker.   

Abstract

PURPOSE: Increasingly, cancer treatment centers need to be able to estimate specific costs and resources associated with clinical trials. Because the time requirements of trial coordination and data collection are not well known, the Clinical Research Associates (CRA) Committee of the National Cancer Institute of Canada Clinical Trials Group carried out a multicenter study to measure trials' task times and evaluate the effects of certain factors.
METHODS: A data collection instrument was designed and validated before its implementation in the study. Eighty-three CRAs from 24 cancer treatment institutions across Canada collected timing observations of 41 tasks (156 subtasks). Information from all stages of trials activity (protocol management, eligibility and entry, treatment, and follow-up and final stage) was obtained, from initial negotiations to follow-up after study closure.
RESULTS: After controlling for stage, phase and sponsor were found to be significant independent factors. Analysis within the stages showed similar patterns. New drug inclusion as a factor was confounded with phase. Industry-sponsored studies had significantly higher overall mean times than did local and cooperative group studies. Early-phase studies required more time than did phase III trials. External sponsorship of any kind increased CRA time more than that necessary for locally coordinated studies, except during the protocol management stage. The burden of a phase I study increased to greater than average once underway and accruing patients.
CONCLUSION: Our data demonstrated that sponsor and study phase are important factors to be taken into consideration when estimating clinical trial costs and resource use.

Entities:  

Mesh:

Year:  2002        PMID: 11786585     DOI: 10.1200/JCO.2002.20.2.545

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  20 in total

1.  Optimizing collection of adverse event data in cancer clinical trials supporting supplemental indications.

Authors:  Lee D Kaiser; Allen S Melemed; Alaknanda J Preston; Hilary A Chaudri Ross; Donna Niedzwiecki; Gwendolyn A Fyfe; Jacqueline M Gough; William D Bushnell; Cynthia L Stephens; M Kelsey Mace; Jeffrey S Abrams; Richard L Schilsky
Journal:  J Clin Oncol       Date:  2010-10-04       Impact factor: 44.544

2.  Unintended consequences of evolution of the Common Terminology Criteria for Adverse Events.

Authors:  Tamara P Miller; Brian T Fisher; Kelly D Getz; Leah Sack; Hanieh Razzaghi; Alix E Seif; Rochelle Bagatell; Peter C Adamson; Richard Aplenc
Journal:  Pediatr Blood Cancer       Date:  2019-04-09       Impact factor: 3.167

3.  Using electronic medical record data to report laboratory adverse events.

Authors:  Tamara P Miller; Yimei Li; Kelly D Getz; Jesse Dudley; Evanette Burrows; Jeffrey Pennington; Azada Ibrahimova; Brian T Fisher; Rochelle Bagatell; Alix E Seif; Robert Grundmeier; Richard Aplenc
Journal:  Br J Haematol       Date:  2017-02-01       Impact factor: 6.998

4.  Measuring clinical trial-associated workload in a community clinical oncology program.

Authors:  Marjorie J Good; Barbara Lubejko; Keisha Humphries; Andrea Medders
Journal:  J Oncol Pract       Date:  2013-02-19       Impact factor: 3.840

5.  Evaluation of the value of attribution in the interpretation of adverse event data: a North Central Cancer Treatment Group and American College of Surgeons Oncology Group investigation.

Authors:  Shauna L Hillman; Sumithra J Mandrekar; Brian Bot; Ronald P DeMatteo; Edith A Perez; Karla V Ballman; Heidi Nelson; Jan C Buckner; Daniel J Sargent
Journal:  J Clin Oncol       Date:  2010-05-17       Impact factor: 44.544

6.  Induction mortality and resource utilization in children treated for acute myeloid leukemia at free-standing pediatric hospitals in the United States.

Authors:  Marko Kavcic; Brian T Fisher; Yimei Li; Alix E Seif; Kari Torp; Dana M Walker; Yuan-Shung Huang; Grace E Lee; Sarah K Tasian; Marijana Vujkovic; Rochelle Bagatell; Richard Aplenc
Journal:  Cancer       Date:  2013-02-21       Impact factor: 6.860

7.  Center-level variation in accuracy of adverse event reporting in a clinical trial for pediatric acute myeloid leukemia: a report from the Children's Oncology Group.

Authors:  Tamara P Miller; Yimei Li; Marko Kavcic; Kelly D Getz; Yuan-Shun V Huang; Lillian Sung; Todd A Alonzo; Robert Gerbing; Marla H Daves; Terzah M Horton; Michael A Pulsipher; Jessica Pollard; Rochelle Bagatell; Alix E Seif; Brian T Fisher; Selina Luger; Alan S Gamis; Peter C Adamson; Richard Aplenc
Journal:  Haematologica       Date:  2017-06-22       Impact factor: 9.941

8.  Ontario protocol assessment level: clinical trial complexity rating tool for workload planning in oncology clinical trials.

Authors:  Bobbi Smuck; Phyllis Bettello; Koralee Berghout; Tracie Hanna; Brenda Kowaleski; Lynda Phippard; Diana Au; Kay Friel
Journal:  J Oncol Pract       Date:  2011-03       Impact factor: 3.840

Review 9.  Compliance in early-phase cancer clinical trials research.

Authors:  Razelle Kurzrock; David J Stewart
Journal:  Oncologist       Date:  2013-03-01

10.  Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.

Authors:  Seung-Mi Lee; Jin-Oh Kang; Yong-Moo Suh
Journal:  J Korean Med Sci       Date:  2004-10       Impact factor: 2.153

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