| Abstract: |
Introduction: Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation. Methods: The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data. Results: Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference. Conclusions: Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data. Trial Registration: CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285. © 2023, The Author(s). |
