Accounting for Heterogeneity in Resource Allocation Decisions: Methods and Practice in UK Cancer Technology Appraisals

Abstract

Objectives: The availability of novel, more efficacious and expensive cancer therapies is increasing, resulting in significant treatment effect heterogeneity and complicated treatment and disease pathways. The aim of this study is to review the extent to which UK cancer technology appraisals (TAs) consider the impact of patient and treatment effect heterogeneity.

Methods: A systematic search of National Institute for Health and Care Excellence TAs of colorectal, lung and ovarian cancer was undertaken for the period up to April 2020. For each TA, the pivotal clinical studies and economic evaluations were reviewed for considerations of patient and treatment effect heterogeneity. The study critically reviews the use of subgroup analysis and real-world translation in economic evaluations, alongside specific attributes of the economic modeling framework.

Results: The search identified 49 TAs including 49 economic models. In total, 804 subgroup analyses were reported across 69 clinical studies. The most common stratification factors were age, gender, and Eastern Cooperative Oncology Group performance score, with 15% (119 of 804) of analyses demonstrating significantly different clinical outcomes to the main population; economic subgroup analyses were undertaken in only 17 TAs. All economic models were cohort-level with the majority described as partitioned survival models (39) or Markov/semi-Markov models. The impact of real-world heterogeneity on disease progression estimates was only explored in 2 models.

Conclusion: The ability of current modeling approaches to capture patient and treatment effect heterogeneity is constrained by their limited flexibility and simplistic nature. This study highlights a need for the use of more sophisticated modeling methods that enable greater consideration of real-world heterogeneity.

Keywords: HTA; NICE; cancer; economic; heterogeneity; modeling; review.