Applying State-of-the-Art Survival Extrapolation Techniques to the Evaluation of CAR-T Therapies: Evidence from a Systematic Literature Review.

INTRODUCTION: Traditional statistical techniques for extrapolating short-term survival data for anticancer therapies assume the same mortality rate for noncured and "cured" patients, which is appropriate for projecting survival of non-curative therapies but may lead to an underestimation of the treatment effectiveness for potentially curative therapies. Our objective was to ascertain research trends in survival extrapolation techniques used to project the survival benefits of chimeric antigen receptor T cell (CAR-T) therapies. METHODS: A global systematic literature search produced a review of survival analyses of CAR-T therapies, published between January 1, 2015 and December 14, 2020, based on publications sourced from MEDLINE, scientific conferences, and health technology assessment agencies. Trends in survival extrapolation techniques used, and the rationale for selecting advanced techniques, are discussed. RESULTS: Twenty publications were included, the majority of which (65%, N = 13) accounted for curative intent of CAR-T therapies through the use of advanced extrapolation techniques, i.e., mixture cure models [MCMs] (N = 10) or spline-based models (N = 3). The authors' rationale for using the MCM approach included (a) better statistical fits to the observed Kaplan-Meier curves (KMs) and (b) visual inspection of the KMs indicated that a proportion of patients experienced long-term remission and survival which is not inherently captured in standard parametric distributions. DISCUSSION: Our findings suggest that an advanced extrapolation technique should be considered in base case survival analyses of CAR-T therapies when extrapolating short-term survival data to long-term horizons extending beyond the clinical trial duration. CONCLUSION: Advanced extrapolation techniques allow researchers to account for the proportion of patients with an observed plateau in survival from clinical trial data; by only using standard-partitioned modeling, researchers may risk underestimating the survival benefits for the subset of patients with long-term remission. Sensitivity analysis with an alternative advanced extrapolation technique should be implemented and re-assessment using clinical trial extension data and/or real-world data should be conducted as longer-term data become available.