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

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

BACKGROUND

Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations.

OBJECTIVES

We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL.

METHODS

OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL.

RESULTS

For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL.

CONCLUSIONS

Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

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

Background

Our aim was to extend traditional parametric models used to extrapolate survival in cost-effectiveness analyses (CEAs) by integrating individual-level patient data (IPD) from a clinical trial with estimates from experts regarding long-term survival. This was illustrated using a case study evaluating survival of patients with triple-class exposed relapsed/refractory multiple myeloma treated with the chimeric antigen receptor (CAR) T cell therapy idecabtagene vicleucel (ide-cel, bb2121) in KarMMa (a phase 2, single-arm trial).

Methods

The distribution of patients expected to be alive at 3, 5, and 10 years given the observed survival from KarMMa (13.3 months of follow-up) was elicited from 6 experts using the SHeffield ELicitation Framework. Quantities of interest were elicited from each expert individually, which informed the consensus elicitation including all experts. Estimates for each time point were assumed to follow a truncated normal distribution. These distributions were incorporated into survival models, which constrained the expected survival based on standard survival distributions informed by IPD from KarMMa.

Results

Models for ide-cel that combined KarMMa data with expert opinion were more consistent in terms of survival as well as mean survival at 10 years (survival point estimates under different parametric models were 29–33% at 3 years, 5–17% at 5 years, and 0–6% at 10 years) versus models with KarMMa data alone (11–39% at 3 years, 0–25% at 5 years, and 0–11% at 10 years).

Conclusion

This case study demonstrates a transparent approach to integrate IPD from trials with expert opinion using traditional parametric distributions to ensure long-term survival extrapolations are clinically plausible.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01745-z.

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

There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.

Challenges in the market access of regenerative medicines, and implications for manufacturers and decision-makers: a systematic review

Aim: Regenerative medicines (RMs) are expected to transform the treatment paradigm of rare, life-threatening diseases, while substantial challenges impede its market access. This study aimed to present these challenges. Materials & methods: Publications identified in the Medline and Embase databases until December 2020 were included. Results: Uncertainties around the relative effectiveness and long-term benefits of RMs are most scrutinized. A new reference case for RMs is questionable, but examining impacts of study perspective, time horizon, discount rate and extrapolation methods on estimates is advised. Establishing reasonable prices of RMs requires increased transparency in the development costs and better values measurements. Outcome-based payments require considerable investments and potential legislative adjustments. Conclusion: Greater flexibility for health technology assessment and economic analyses of RMs is necessary. This comprehensive review may prompt more multi-stakeholder conversations to discuss the optimized strategy for value assessment, pricing and payment in order to accelerate the market access of RMs.