Elsevier

Value in Health

Volume 26, Issue 2, February 2023, Pages 185-192
Value in Health

Comparative-Effectiveness Research/HTA
A Guide to Selecting Flexible Survival Models to Inform Economic Evaluations of Cancer Immunotherapies

https://doi.org/10.1016/j.jval.2022.07.009Get rights and content
Under a Creative Commons license
open access

Highlights

  • Standard parametric survival models can lack the flexibility to capture the shape of the underlying hazard functions of patients treated with cancer immunotherapies. This can lead to inaccurate long-term survival projections and biased cost-effectiveness estimates. More flexible extrapolation models are being adopted, but their acceptability for healthcare decision making can be an area of contention. Guidance is limited on when flexible models should be used and which, of the many available, should be considered.

  • An algorithm was developed to supplement existing guidance on extrapolation model selection for economic evaluation. The algorithm recommends an initial review of relevant external evidence identified in a systematic, reproducible way; highlights the need to engage with clinical experts and consider the observed hazard function and how it might change in the future; cautions against overinterpreting observed survival, particularly when data are immature; and recommends that the results of all plausible models are presented.

  • If followed, this algorithm will provide a systematic and evidence-based approach for flexible survival model selection. This will improve transparency and consistency, reduce the risk of inappropriate model selection, and increase confidence in the results of the cost-effectiveness analysis of cancer immunotherapies. It may also prove useful for other treatments and diseases where more flexible extrapolation models may be warranted.

Abstract

Objectives

Parametric models are routinely used to estimate the benefit of cancer drugs beyond trial follow-up. The advent of immune checkpoint inhibitors has challenged this paradigm, and emerging evidence suggests that more flexible survival models, which can better capture the shapes of complex hazard functions, might be needed for these interventions. Nevertheless, there is a need for an algorithm to help analysts decide whether flexible models are required and, if so, which should be chosen for testing. This position article has been produced to bridge this gap.

Methods

A virtual advisory board comprising 7 international experts with in-depth knowledge of survival analysis and health technology assessment was held in summer 2021. The experts discussed 24 questions across 6 topics: the current survival model selection procedure, data maturity, heterogeneity of treatment effect, cure and mortality, external evidence, and additions to existing guidelines. Their responses culminated in an algorithm to inform selection of flexible survival models.

Results

The algorithm consists of 8 steps and 4 questions. Key elements include the systematic identification of relevant external data, using clinical expert input at multiple points in the selection process, considering the future and the observed hazard functions, assessing the potential for long-term survivorship, and presenting results from all plausible models.

Conclusions

This algorithm provides a systematic, evidence-based approach to justify the selection of survival extrapolation models for cancer immunotherapies. If followed, it should reduce the risk of selecting inappropriate models, partially addressing a key area of uncertainty in the economic evaluation of these agents.

Keywords

algorithm
cancer
extrapolation
immunotherapy
survival analysis

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