A Primer on Dose-Response Data Modeling in Radiation Therapy.

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  • Additional Information
    • Source:
      Publisher: Elsevier, Inc Country of Publication: United States NLM ID: 7603616 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-355X (Electronic) Linking ISSN: 03603016 NLM ISO Abbreviation: Int J Radiat Oncol Biol Phys Subsets: MEDLINE
    • Publication Information:
      Publication: New York, NY : Elsevier, Inc
      Original Publication: Elmsford, N. Y., Pergamon Press.
    • Subject Terms:
    • Abstract:
      An overview of common approaches used to assess a dose response for radiation therapy-associated endpoints is presented, using lung toxicity data sets analyzed as a part of the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic effort as an example. Each component presented (eg, data-driven analysis, dose-response analysis, and calculating uncertainties on model prediction) is addressed using established approaches. Specifically, the maximum likelihood method was used to calculate best parameter values of the commonly used logistic model, the profile-likelihood to calculate confidence intervals on model parameters, and the likelihood ratio to determine whether the observed data fit is statistically significant. The bootstrap method was used to calculate confidence intervals for model predictions. Correlated behavior of model parameters and implication for interpreting dose response are discussed.
      (Copyright © 2020 Elsevier Inc. All rights reserved.)
    • Comments:
      Comment in: Int J Radiat Oncol Biol Phys. 2021 Jun 1;110(2):609. (PMID: 33989579)
      Comment in: Int J Radiat Oncol Biol Phys. 2021 Aug 1;110(5):1541-1543. (PMID: 34024669)
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    • Grant Information:
      P30 CA008748 United States CA NCI NIH HHS; R01 CA233487 United States CA NCI NIH HHS; R01 CA238783 United States CA NCI NIH HHS
    • Publication Date:
      Date Created: 20201228 Date Completed: 20210804 Latest Revision: 20220802
    • Publication Date:
      20231215
    • Accession Number:
      PMC9339232
    • Accession Number:
      10.1016/j.ijrobp.2020.11.020
    • Accession Number:
      33358230