Designing precision medicine trials to yield a greater population impact.

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  • Author(s): Zhao YQ;Zhao YQ; LeBlanc ML; LeBlanc ML
  • Source:
    Biometrics [Biometrics] 2020 Jun; Vol. 76 (2), pp. 643-653. Date of Electronic Publication: 2019 Nov 07.
  • Publication Type:
    Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • Publication Information:
      Publication: March 2024- : [Oxford] : Oxford University Press
      Original Publication: Alexandria Va : Biometric Society
    • Subject Terms:
      Clinical Trials as Topic/*methods ; Precision Medicine/*methods; Algorithms ; Biomarkers/analysis ; Biomarkers, Tumor/blood ; Biometry ; Clinical Trials as Topic/statistics & numerical data ; Clinical Trials, Phase III as Topic/methods ; Clinical Trials, Phase III as Topic/statistics & numerical data ; Computer Simulation ; Humans ; Linear Models ; Male ; Models, Statistical ; Precision Medicine/statistics & numerical data ; Proportional Hazards Models ; Prostatic Neoplasms, Castration-Resistant/blood ; Prostatic Neoplasms, Castration-Resistant/drug therapy ; Prostatic Neoplasms, Castration-Resistant/pathology ; Sample Size ; Treatment Outcome
    • Abstract:
      Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients' heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker-based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max-Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.
      (© 2019 The International Biometric Society.)
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    • Grant Information:
      P30 CA015704 United States CA NCI NIH HHS; R01 DK108073 United States DK NIDDK NIH HHS; U10 CA180819 United States CA NCI NIH HHS; U10 CA180888 United States CA NCI NIH HHS
    • Contributed Indexing:
      Keywords: biomarkers; population impact; precision medicine; targeted clinical trial design
    • Accession Number:
      0 (Biomarkers)
      0 (Biomarkers, Tumor)
    • Publication Date:
      Date Created: 20191011 Date Completed: 20210826 Latest Revision: 20210826
    • Publication Date:
      20231215
    • Accession Number:
      PMC7211185
    • Accession Number:
      10.1111/biom.13161
    • Accession Number:
      31598964