The use of a predictive statistical model to make a virtual control arm for a clinical trial.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • Additional Information
    • Publication Year:
      2019
    • Author-Supplied Keywords:
      Adjuvant chemotherapy
      Breast cancer
      Breast tumors
      Cancer chemotherapy
      Cancer treatment
      Cancers and neoplasms
      Chemotherapy
      Clinical medicine
      Clinical oncology
      Clinical trials
      Clinical trials (cancer treatment)
      Drug research and development
      Drug therapy
      Forecasting
      Mathematical and statistical techniques
      Mathematics
      Medicine and health sciences
      Oncology
      Pharmaceutics
      Pharmacology
      Phase II clinical investigation
      Phase III clinical investigation
      Physical sciences
      Research and analysis methods
      Research Article
      Statistical methods
      Statistical models
      Statistics
    • NAICS/Industry Codes:
      NAICS/Industry Codes 541712 Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology)
    • Abstract:
      Background: Randomized clinical trials compare participants receiving an experimental intervention to participants receiving standard of care (SOC). If one could predict the outcome for participants receiving SOC, a trial could be designed where all participants received the experimental intervention, with the observed outcome of the experimental group compared to the prediction for those individuals. Methods: We used the CancerMath calculator to predict outcomes for participants in two large clinical trials of adjuvant chemotherapy for breast cancer: NSABPB15 and CALGB9344. NSABPB15 was the training set, and we used the modified algorithm to predict outcomes for two groups from CALGB9344: one which received standard of care (SOC) chemotherapy and one which received paclitaxel in addition. We made a prediction for each individual CALGB9344 participant, assuming each received only SOC. Results: The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. In contrast, the predicted outcome for the group also receiving paclitaxel was significantly worse than what was observed for this group. This matches the conclusion of CALGB9344 that adding paclitaxel to SOC improves survival. Conclusion: This project proves that a statistical model can predict the outcome of clinical trial participants treated with SOC. In some circumstances, a predictive model could be used instead of a control arm, allowing all participants to receive experimental treatment. Predictive models for cancer and other diseases could be constructed using the vast amount of outcomes data available to the federal government, and made available for public use. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • ISSN:
      19326203
    • Accession Number:
      138428193
  • Citations
    • ABNT:
      SWITCHENKO, J. M. et al. The use of a predictive statistical model to make a virtual control arm for a clinical trial. PLoS ONE, [s. l.], v. 14, n. 9, p. 1, 2019. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193. Acesso em: 17 fev. 2020.
    • AMA:
      Switchenko JM, Heeke AL, Pan TC, Read WL. The use of a predictive statistical model to make a virtual control arm for a clinical trial. PLoS ONE. 2019;14(9):1. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193. Accessed February 17, 2020.
    • APA:
      Switchenko, J. M., Heeke, A. L., Pan, T. C., & Read, W. L. (2019). The use of a predictive statistical model to make a virtual control arm for a clinical trial. PLoS ONE, 14(9), 1.
    • Chicago/Turabian: Author-Date:
      Switchenko, Jeffrey M., Arielle L. Heeke, Tony C. Pan, and William L. Read. 2019. “The Use of a Predictive Statistical Model to Make a Virtual Control Arm for a Clinical Trial.” PLoS ONE 14 (9): 1. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193.
    • Harvard:
      Switchenko, J. M. et al. (2019) ‘The use of a predictive statistical model to make a virtual control arm for a clinical trial’, PLoS ONE, 14(9), p. 1. Available at: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193 (Accessed: 17 February 2020).
    • Harvard: Australian:
      Switchenko, JM, Heeke, AL, Pan, TC & Read, WL 2019, ‘The use of a predictive statistical model to make a virtual control arm for a clinical trial’, PLoS ONE, vol. 14, no. 9, p. 1, viewed 17 February 2020, .
    • MLA:
      Switchenko, Jeffrey M., et al. “The Use of a Predictive Statistical Model to Make a Virtual Control Arm for a Clinical Trial.” PLoS ONE, vol. 14, no. 9, Sept. 2019, p. 1. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193.
    • Chicago/Turabian: Humanities:
      Switchenko, Jeffrey M., Arielle L. Heeke, Tony C. Pan, and William L. Read. “The Use of a Predictive Statistical Model to Make a Virtual Control Arm for a Clinical Trial.” PLoS ONE 14, no. 9 (September 4, 2019): 1. http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193.
    • Vancouver/ICMJE:
      Switchenko JM, Heeke AL, Pan TC, Read WL. The use of a predictive statistical model to make a virtual control arm for a clinical trial. PLoS ONE [Internet]. 2019 Sep 4 [cited 2020 Feb 17];14(9):1. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=138428193