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

Abstract

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.

Structured expert elicitation to inform long-term survival extrapolations using alternative parametric distributions: a case study of CAR T therapy for relapsed/ refractory multiple myeloma

“…In this context, The National Institute for Health and Care Excellence (NICE) recommends fitting alternative parametric models to extrapolate survival, where model selection is informed by visual assessment, log-hazard plots, goodness-of-fit statistics, and an evaluation of plausibility of the extrapolations in terms of clinical validity [ 1 , 2 ]. Recently, more flexible parametric models have been recommended for complex survival data [ 3 ], which are increasingly being proposed to assess the expected survival for new interventions, such as immunotherapies [ 4 – 6 ] and chimeric antigen receptor (CAR) T cell therapy [ 7 ]. As more flexible methods are used, the need to consider the plausibility of extrapolations is even more important given that these methods may yield less realistic shapes in terms of long-term hazard [ 3 ].…”

Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products

“…For example, a systematic review of cost-effectiveness models for CAR-T therapies which use survival analyses to extrapolate long-term survival, identified 20 relevant cost-effectiveness models. 39 Of these, 10 used mixture cure models, three used spline-based models to account for the "curative intent" of CAR-T therapies, three used traditional parametric distributions, and the remaining four used microsimulation or optimisation to estimate the proportion of patients in each health state. The National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) in the UK has provided an overview of several of these survival model approaches; 32 however, the appropriateness of models for complex hazard functions is not yet fully understood and is a rapidly evolving area of research.…”

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma

“…An understanding of patient outcomes beyond the maximum follow-up can have salient implications for future treatment options, as well as a profound impact on cost of care. Survival modeling can be a useful tool in extrapolating future outcomes based on available data, and has been used to inform cost-effectiveness analyses [1]. To determine the full clinical and economic value of a new treatment, extrapolation of observed survival is routinely required, and different extrapolation methods may lead to substantive variation in total survival gain and consequent value estimation.…”