Modelling approaches for histology-independent cancer drugs to inform NICE appraisals: a systematic review and

Peter Murphy1, David Glynn2, Sofia Dias1

  • 1Centre for Reviews and Dissemination, University of York, York, UK.

Abstract

Insights

Histology-independent cancer treatments approved based on biomarkers present unique challenges for health technology appraisals. Bayesian methods and careful trial design are recommended to address heterogeneity and inform decision-making for these novel therapies.

Area of Science:

  • Oncology
  • Health Technology Assessment
  • Biostatistics

Background:

  • First European marketing authorisation for a histology-independent cancer treatment granted in 2019.
  • Approval based on common biomarkers, not tumour location.
  • Research explores implications for National Institute for Health and Care Excellence (NICE) appraisals.

Purpose of the Study:

  • To explore implications of histology-independent cancer treatments for NICE appraisals.
  • To identify evidence requirements and analytical approaches for NICE appraisals.
  • To develop a decision framework for histology-independent products.

Main Methods:

  • Targeted reviews of evidence at marketing authorisation.
  • Analysis of statistical literature on histology-independent trial design and analysis.
  • Systematic review of response end points as surrogates for survival.
  • Development of a decision framework for approval and research policies.

Main Results:

  • Heterogeneity in treatment effects across tumour types is a key issue for NICE appraisals.
  • Bayesian hierarchical methods can assess heterogeneity and estimate pooled treatment effects.
  • Response end points may not be reliable surrogates for survival; surrogate-based modelling is preferable.
  • Other challenges include testing costs, baseline risk, quality of life, and management costs.

Conclusions:

  • Basket studies assessing homogeneity and allowing information borrowing are recommended.
  • Optimised recommendations should consider heterogeneity in treatment effects and cost-effectiveness.
  • Routine presentation of heterogeneity consequences and decision uncertainty is advised.