A Review of Overall Survival Extrapolations of Immune-Checkpoint Inhibitors Used in Health Technology Assessments by the French Health Authorities

Assessing the Performance of Alternative Methods for Estimating Long-Term Survival Benefit of Immuno-oncology Therapies

OBJECTIVES

This study aimed to determine the accuracy and consistency of established methods of extrapolating mean survival for immuno-oncology (IO) therapies, the extent of any systematic biases in estimating long-term clinical benefit, what influences the magnitude of any bias, and the potential implications for health technology assessment.

METHODS

A targeted literature search was conducted to identify published long-term follow-up from clinical trials of immune-checkpoint inhibitors. Earlier published results were identified and Kaplan-Meier estimates for short- and long-term follow-up were digitized and converted to pseudo-individual patient data using an established algorithm. Six standard parametric, 5 flexible parametric, and 2 mixture-cure models (MCMs) were used to extrapolate long-term survival. Mean and restricted mean survival time (RMST) were estimated and compared between short- and long-term follow-up.

RESULTS

Predicted RMST from extrapolation of early data underestimated observed RMST in long-term follow-up for 184 of 271 extrapolations. All models except the MCMs frequently underestimated observed RMST. Mean survival estimates increased with longer follow-up in 196 of 270 extrapolations. The increase exceeded 20% in 122 extrapolations. Log-logistic and log-normal models showed the smallest change with additional follow-up. MCM performance varied substantially with functional form.

CONCLUSIONS

Standard and flexible parametric models frequently underestimate mean survival for IO treatments. Log-logistic and log-normal models may be the most pragmatic and parsimonious solutions for estimating IO mean survival from immature data. Flexible parametric models may be preferred when the data used in health technology assessment are more mature. MCMs fitted to immature data produce unreliable results and are not recommended.

Clinical benefit of anti-PD-(L)1 immunotherapies in advanced cancer in France: a population-based estimate from 2014 to 2021

BACKGROUND

In France, the potential benefit of new treatments is initially evaluated by the Haute Autorité de Santé to determine reimbursement and pricing, but rarely afterwards. Although immunotherapies (ITs) have considerably improved the survival of patients, few data are available on their long-term benefit at a population-treated level. The present retrospective study aimed to assess the clinical benefit of ITs compared to the previous standards of care (SoCs) in France from 2014 to 2021.

MATERIALS AND METHODS

To do this, we analyzed all ITs from the anti-programmed cell death protein 1/programmed death-ligand 1 [anti-PD-(L)1] class used in monotherapy or in association with another treatment available in early access or reimbursed in France between 2014 and 2021, regardless of indication. The number of patients initiating an IT was retrieved by year, drug and indication. Using extrapolated Kaplan-Meier curves, utility scores and the population treated, the clinical benefit was expressed as the number of deaths prevented (DP), life-years (LYs) and quality-adjusted life years (QALYs) gained compared to previous SoC.

RESULTS

Across the period, five ITs were marketed in 21 indications related to eight primary tumor sites. Between 2014 and 2021, 132 924 patients initiated an IT. By December 2021, 16 173 (13 804-17 141) deaths were delayed compared to previous SoC, mainly in lung cancer. Compared to their SoC, ITs provided a gain of 37 316 (33 581-41 048) additional LYs and 27 709 (23 784-30 450) additional QALYs. Lung cancer was the driver indication with 70.6% of LYs and 68.4% of QALYs gained followed by melanoma with 18.7% and 20.4% of the gain, respectively.

CONCLUSIONS

Significant gains in DP, LYs and QALYs have been observed in France following the introduction of ITs.

Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067

OBJECTIVES

The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy.

METHODS

The individual patient data was reconstructed through published Checkmate 067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston-Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub.

RESULTS

The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles.

CONCLUSIONS

Flexible techniques show better performance in the case of Checkmate 067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the 'right' model, we recommend identifying and revealing the model impacts on structural uncertainty.