Treatment Effect Reanalysis of the Randomized Individual Screening Trial of Innovative Glioblastoma Therapy in Newly Diagnosed Glioblastoma With External Control Data
Abstract
PURPOSE Integrating external control data into clinical trial designs and analyses has the potential to accelerate drug development processes. We reanalyzed the three experimental arms of the Individual Screening Trial of Innovative Glioblastoma Therapy (INSIGhT), a randomized phase II platform trial in newly diagnosed O 6 -methylguanine-DNA methyltransferase-unmethylated glioblastoma (ClinicalTrials.gov identifier: NCT02977780 ). To evaluate the validity of using external data sets, we compared treatment effect estimates based on internal INSIGhT control data and matched external control data. METHODS The three experimental arms of INSIGhT (abemaciclib [n = 72], neratinib [n = 80], and CC-115 [n = 12]) did not improve survival compared with internal controls (standard chemoradiation [n = 70]). We derived external control patient-level data from multiple real-world and clinical trial data sets. We applied propensity score matching and Cox proportional hazards models to estimate treatment effects with external controls. Additionally, using this glioblastoma (GBM) data collection, we specified simulation scenarios to evaluate trial designs that integrate external controls. RESULTS After matching to external controls, no survival benefit was observed for patients receiving abemaciclib (hazard ratio [HR], 1.00 [95% CI, 0.75 to 1.34]), neratinib (HR, 0.93 [95% CI, 0.70 to 1.24]), or CC-115 (HR, 0.88 [95% CI, 0.41 to 1.88]). Simulations, together with the INSIGhT data and a collection of GBM data sets, allowed us to examine efficiencies and risks of clinical trial designs that leverage external control data. CONCLUSION The use of carefully matched external controls, to replace or augment the internal controls of INSIGhT, produced treatment effect estimates that were similar to previously published analyses. Single-arm trial designs and hybrid randomized designs incorporating propensity score–matched external control data evaluated treatment effects in the early-phase testing of experimental therapies in newly diagnosed GBM. The validity of this approach and risks of bias depended on the availability of comprehensive and accurate data on all potential confounders, in the absence of unmeasured confounding.
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (42)
Tulika Rudra Gupta
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA
Mei-Yin C. Polley
Department of Public Health Sciences, University of Chicago, Chicago, IL
Robert Redd
Eudocia Q. Lee
Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA
Isabel Arrillaga-Romany
Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, MA
Mark R. Gilbert
National Cancer Institute, Bethesda, MD
Yujue Tan
Brigham and Women's Hospital, Boston, MA
Mehdi Touat
Jan Drappatz
22Department of Neurology and Medicine, University of Pittsburgh, Pittsburgh, PA
Mary R. Welch
Division of Neuro-Oncology, Department of Neurology and Herbert Irving Comprehensive Cancer Center, Columbia University Vagelos College of Physicians and Surgeons, New York-Presbyterian, New York, NY
Evanthia Galanis
Mayo Clinic, Rochester, MN
Manmeet S. Ahluwalia
Howard Colman
L. Burt Nabors
University of Alabama at Birmingham, Birmingham, AL
Jaroslaw Hepel
Rhode Island Hospital, Providence, RI
Heinrich Elinzano
Rhode Island Hospital, Providence, RI
David Schiff
31Department of Neurology, Division of Neuro-Oncology, University of Virginia, Charlottesville, VA
Ugonma N. Chukwueke
Rameen Beroukhim
Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard
Lakshmi Nayak
4Department of Medical Oncology, Center for Neuro Oncology, Dana Farber Cancer Institute, Boston, MA
J. Ricardo McFaline-Figueroa
New York University, New York, NY
Tracy T. Batchelor
Thomas J. Kaley
Memorial Sloan Kettering Cancer Center, New York, NY
Christine Lu-Emerson
Maine Medical Center, Portland, ME
Ingo K. Mellinghoff
Memorial Sloan Kettering Cancer Center, New York, NY
Wenya Linda Bi
Omar Arnaout
Pier Paolo Peruzzi
Daphne Haas-Kogan
Brigham and Women's Hospital, Boston, MA
Shyam Tanguturi
Brigham and Women's Hospital, Boston, MA
Ayal Aizer
Brigham and Women's Hospital, Boston, MA
Lisa Doherty
Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA
Sandro Santagata
David M. Meredith
E. Antonio Chiocca
David A. Reardon
Keith L. Ligon
Michael Weller
Minesh P. Mehta
Patrick Y. Wen
Lorenzo Trippa
From Médecins Sans Frontières (L.G., F.V.), Sorbonne Université, INSERM Unité 1135, Centre d’Immunologie et des Maladies Infectieuses (L.G.), Assistance Publique–Hôpitaux de Paris, Groupe Hospitalier Universitaire Sorbonne Université, Hôpital Pitié–Salpêtrière, Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux (L.G.), and Epicentre (M.G., E. Baudin), Paris, and Translational Research on HIV and Endemic and Emerging Infectious Diseases, Montpellier Université de Montpellier, Montpellier, Institut de Recherche pour le Développement, Montpellier, INSERM, Montpellier (M.B.) — all in France; Interactive Development and Research, Singapore (U.K.); McGill University, Epidemiology, Biostatistics, and Occupational Health, Montreal (U.K.); UCSF Center for Tuberculosis (G.E.V., P.N., P.P.J.P.) and the Division of HIV, Infectious Diseases, and Global Medicine (G.E.V.), University of California at San Francisco, San Francisco; the National Scientific Center of Phth...
Rifaquat Rahman
Brigham and Women's Hospital, Boston, MA