4.7 Article

Precision oncology: the intention-to-treat analysis fallacy

期刊

EUROPEAN JOURNAL OF CANCER
卷 133, 期 -, 页码 25-28

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ejca.2020.04.002

关键词

Precision oncology; Intention-to-treat

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资金

  1. Foundation Medicine Inc.
  2. Joan and Irwin Jacobs Philanthropic Fund
  3. Jon Schneider Memorial Cancer Research Fund
  4. NIH [K08CA168999, R21CA192072, R01CA226803, P30CA023100]
  5. Pedal the Cause
  6. David Foundation
  7. Kristen Ann Carr Fund

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It has recently been suggested that precision oncology studies should be reanalysed using the intention-to-treat (ITT) methodology developed for randomized controlled clinical trials. This reanalysis dramatically decreases response rates in precision medicine studies. We contend that the ITT analysis of precision oncology trials is invalid. The ITT methodology was developed three decades ago to mitigate the problems of randomized trials, which try to ensure that both arms have an unselected patient population free from confounders. In contrast, precision oncology trials specifically select patients for confounders (that is biomarkers) that predict response. To demonstrate the issues inherent in an ITT reanalysis for precision cancer medicine studies, we take as an example the drug larotrectinib (TRK inhibitor) approved because of remarkable responses in malignancies harbouring NTRK fusions. Based on large-scale studies, NTRK fusions are found in similar to 0.31% of tumours. In a non-randomized pivotal study of larotrectinib, 75% of the 55 treated patients responded. Based upon the prevalence of NTRK fusions, similar to 18,000 patients would need to be screened to enrol the 55 treated patients. Utilizing the ITT methodology, the revised response rate to larotrectinib would be 0.23%. This is, of course, a dramatic underestimation of the efficacy of this now Food and Drug Administration (FDA)-approved drug. Similar issues can be shown for virtually any biomarker-based precision clinical trial. Therefore, retrofitting the ITT analysis developed for unselected patient populations in randomized trials yields misleading conclusions in precision medicine studies. (C) 2020 Elsevier Ltd. All rights reserved.

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