Jinani Jayasekera1, Yisheng Li2, Clyde B Schechter3, Reshma Jagsi4, Juhee Song2, Julia White5, George Luta6, Judith-Anne W Chapman7, Eric J Feuer8, Richard C Zellars9, Natasha Stout10, Thomas B Julian11, Timothy Whelan12, Xuelin Huang2, E Shelley Hwang13, Judith O Hopkins14, Joseph A Sparano3, Stewart J Anderson15, Anthony W Fyles16, Robert Gray17, Willi Sauerbrei18, Jeanne Mandelblatt1, Donald A Berry2. 1. Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC. 2. Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX. 3. Departments of Family and Social Medicine and Epidemiology and Population Health and Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY. 4. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI. 5. Department of Radiation Oncology, The James, The Ohio State University Comprehensive Cancer Center, Columbus, OH. 6. Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC. 7. Canadian Cancer Trials Group, Queen's University, Kingston, ON, Canada. 8. Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD. 9. Department of Radiation Oncology, Indiana University, Indianapolis, IN. 10. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA. 11. NRG Oncology, and the Division of Breast Surgical Oncology, Allegheny General Hospital, Allegheny Health Network, Pittsburgh, PA. 12. McMaster University and Hamilton Heath Sciences, Hamilton, ON, Canada. 13. Department of Surgery, Duke Cancer Institute, Duke University Medical School, Chapel Hill, NC. 14. Novant Health Oncology Specialists, Winston-Salem, NC. 15. NRG Oncology, and the Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA. 16. Cancer Clinical Research Unit, University of Toronto Princess Margaret Cancer Centre, Toronto, ON, Canada. 17. Department of Biostatistics at Harvard University and Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute, Boston, MA. 18. Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Abstract
Background: We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates. Methods: The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively. Results: Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results. Conclusions: The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient.
RCT Entities:
Background: We used two models to simulate a proposed noninferiority trial of radiotherapy (RT) omission in low-risk invasive breast cancer to illustrate how modeling could be used to predict the trial's outcomes, inform trial design, and contribute to practice debates. Methods: The proposed trial was a prospective randomized trial of no-RT vs RT in women age 40 to 74 years undergoing lumpectomy and endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, stage I breast cancer with an Oncotype DX score of 18 or lower. The primary endpoint was recurrence-free interval (RFI), including locoregional recurrence, distant recurrence, and breast cancer death. Noninferiority required the two-sided 90% confidence interval of the RFI hazard ratio (HR) for no-RT vs RT to be entirely below 1.7. Model inputs included published data. The trial was simulated 1000 times, and results were summarized as percent concluding noninferiority and mean (standard deviation) of hazard ratios for Model GE and Model M, respectively. Results: Noninferiority was demonstrated in 18.0% and 3.7% for the two models. The respective means (SD) of the RFI hazard ratios were 1.8 (0.7) and 2.4 (0.9); most were locoregional recurrences. The mean five-year RFI rates for no-RT vs RT (SD) were 92.7% (2.9%) vs 95.5% (2.2%) and 88.4% (2.0%) vs 94.5% (1.6%). Both models showed little or no difference in breast cancer-specific or overall survival. Alternative definitions of low risk based on combinations of age and grade produced similar results. Conclusions: The proposed trial was unlikely to show noninferiority of omitting radiotherapy even using alternative definitions of low-risk, as the endpoint included local recurrence. Future trials regarding radiotherapy should address absolute reduction in recurrence and impact of type of recurrence on the patient.
Authors: Barbara C Sorkin; Adam J Kuszak; Gregory Bloss; Naomi K Fukagawa; Freddie Ann Hoffman; Mahtab Jafari; Bruce Barrett; Paula N Brown; Frederic D Bushman; Steven J Casper; Floyd H Chilton; Christopher S Coffey; Mario G Ferruzzi; D Craig Hopp; Mairead Kiely; Daniel Lakens; John B MacMillan; David O Meltzer; Marco Pahor; Jeffrey Paul; Kathleen Pritchett-Corning; Sara K Quinney; Barbara Rehermann; Kenneth D R Setchell; Nisha S Sipes; Jacqueline M Stephens; D Lansing Taylor; Hervé Tiriac; Michael A Walters; Dan Xi; Giovanna Zappalá; Guido F Pauli Journal: FASEB J Date: 2019-12-10 Impact factor: 5.834
Authors: Gordon P Watt; Anne S Reiner; Susan A Smith; Daniel O Stram; Marinela Capanu; Kathleen E Malone; Charles F Lynch; Esther M John; Julia A Knight; Lene Mellemkjær; Leslie Bernstein; Jennifer D Brooks; Meghan Woods; Xiaolin Liang; Robert W Haile; Nadeem Riaz; David V Conti; Mark Robson; David Duggan; John D Boice; Roy E Shore; Marc Tischkowitz; Irene Orlow; Duncan C Thomas; Patrick Concannon; Jonine L Bernstein Journal: JAMA Netw Open Date: 2019-09-04
Authors: Amy Trentham-Dietz; Oguzhan Alagoz; Christina Chapman; Xuelin Huang; Jinani Jayasekera; Nicolien T van Ravesteyn; Sandra J Lee; Clyde B Schechter; Jennifer M Yeh; Sylvia K Plevritis; Jeanne S Mandelblatt Journal: PLoS Comput Biol Date: 2021-06-17 Impact factor: 4.475
Authors: Jinani Jayasekera; Joseph A Sparano; Robert Gray; Claudine Isaacs; Allison Kurian; Suzanne O'Neill; Clyde B Schechter; Jeanne Mandelblatt Journal: JNCI Cancer Spectr Date: 2019-08-10