Economic Evaluation in Stratified Medicine: Methodological Issues and Challenges

Next-generation sequencing in oncology: challenges in economic evaluations

INTRODUCTION

Next-generation sequencing (NGS) identifies genetic variants to inform personalized treatment plans. Insufficient evidence of cost-effectiveness impedes integration of NGS into routine cancer care. The complexity of personalized treatment challenges conventional economic evaluation. Clearly delineating challenges informs future cost-effectiveness analyses to better value and contextualize health, preference-, and equity-based outcomes.

AREAS COVERED

We conducted a scoping review to characterize the applied methods and outcomes of economic evaluations of NGS in oncology and identify existing challenges. We included 27 articles published since 2016 from a search of PubMed, Embase, and Web of Science. Identified challenges included defining the evaluative scope, managing evidentiary limitations including lack of causal evidence, incorporating preference-based utility, and assessing distributional and equity-based impacts. These challenges reflect the difficulty of generating high-quality clinical effectiveness and real-world evidence (RWE) for NGS-guided interventions.

EXPERT OPINION

Adapting methodological approaches and developing life-cycle health technology assessment (HTA) guidance using RWE is crucial for implementing NGS in oncology. Healthcare systems, decision-makers, and HTA organizations are facing a pivotal opportunity to adapt to an evolving clinical paradigm and create innovative regulatory and reimbursement processes that will enable more sustainable, equitable, and patient-oriented healthcare.

Economic evaluation of genomic/genetic tests: a review and future directions

It has been suggested that health economists need to improve their methods in order to meet the challenges of evaluating genomic/genetic tests. In this article, we set out twelve challenges identified from a rapid review of the literature and suggest solutions to the challenges identified. Two challenges were common to all economic evaluations: choice of perspective and time-horizon. Five challenges were relevant for all diagnostic technologies: complexity of analysis; range of costs; under-developed evidence base; behavioral aspects; and choice of outcome metrics. The final five challenges were pertinent for genomic tests and only these may require methodological development: heterogeneity of tests and platforms, increasing stratification, capturing personal utility; incidental findings; and spillover effects. Current methods of economic evaluation are generally able to cope with genomic/genetic tests, although a renewed focus on specific decision-makers’ needs and a willingness to move away from cost-utility analysis may be required. Certain analysts may be constrained by reference cases developed primarily for the assessment of pharmaceuticals. The combined impact of multiple challenges may require analysts to be particularly careful in setting the scope of their analysis in order to ensure that feasibility is balanced with usefulness to the decision maker. A key issue is the under-developed evidence-base and it may be necessary to rethink translation processes to ensure sufficient, relevant evidence is available to support economic evaluation and adoption of genomic/genetic tests.

Early cost-effectiveness modeling for better decisions in public research investment of personalized medicine technologies

Millions of dollars are spent on the development of new personalized medicine technologies. While these research costs are often supported by public research funds, many diagnostic tests and biomarkers are not adopted by the healthcare system due to lack of evidence on their cost-effectiveness. We describe a stepwise approach to conducting cost-effectiveness analyses that are performed early in the technology's development process and can help mitigate the potential risks of investment. Decision analytic modeling can identify the key drivers of cost effectiveness and provide minimum criteria that the technology needs to meet for adoption by public and private healthcare systems. A value of information analysis can quantify the added value of conducting more research to provide further evidence for policy decisions. These steps will allow public research funders to make better decisions on their investments to maximize the health benefits and to minimize the number of suboptimal technologies.

The Future of Precision Medicine: Potential Impacts for Health Technology Assessment

OBJECTIVE

Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies.

METHODS

We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research.

RESULTS

We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and 'omics'-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects.

DISCUSSION

Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.

Methodological Issues in Assessing the Economic Value of Next-Generation Sequencing Tests: Many Challenges and Not Enough Solutions

BACKGROUND

Clinical use of next-generation sequencing (NGS) tests has been increasing, but few studies have examined their economic value. Several studies have noted that there are methodological challenges to conducting economic evaluations of NGS tests.

OBJECTIVE

Our objective was to examine key methodological challenges for conducting economic evaluations of NGS tests, prioritize these challenges for future research, and identify how studies have attempted solutions to address these challenges.

METHODS

We identified challenges for economic evaluations of NGS tests using prior literature and expert judgment of the co-authors. We used a modified Delphi assessment to prioritize challenges, based on importance and probability of resolution. Using a structured literature review and article extraction we then assessed whether published economic evaluations had addressed these challenges.

RESULTS

We identified 11 challenges for conducting economic evaluations of NGS tests. The experts identified three challenges as the top priorities for future research: complex model structure, timeframe, and type of analysis and comparators used. Of the 15 published studies included in our literature review, four studies described specific solutions relevant to five of the 11 identified challenges.

CONCLUSIONS

Major methodological challenges to economic evaluations of NGS tests remain to be addressed. Our results can be used to guide future research and inform decision-makers on how to prioritize research on the economic assessment of NGS tests.

Cost of cancer diagnosis using next-generation sequencing targeted gene panels in routine practice: a nationwide French study

It is currently unclear if next-generation sequencing (NGS) technologies can be implemented in the diagnosis setting at an affordable cost. The aim of this study was to measure the total cost of performing NGS in clinical practice in France, in both germline and somatic cancer genetics.The study was performed on 15 French representative cancer molecular genetics laboratories performing NGS panels' tests. The production cost was estimated using a micro-costing method with resources consumed collected in situ in each laboratory from a healthcare provider perspective. In addition, we used a top-down methodology for specific post-sequencing steps including bioinformatics, technical validation, and biological validation. Additional non-specific costs were also included. Costs were detailed per step of the process (from the pre-analytical phase to delivery of results), and per cost driver (consumables, staff, equipment, maintenance, overheads). Sensitivity analyses were performed.The mean total cost of NGS for targeted gene panels was estimated to 607€ (±207) in somatic genetics and 550€ (±140) in germline oncogenetic analysis. Consumables were the highest cost driver of the sequencing process. The sensitivity analysis showed that a 25% reduction of consumables resulted in a 15% decrease in total NGS cost in somatic genetics, and 13% in germline analysis. Additional costs accounted for 30-32% of the total NGS costs.Beyond cost assessment considerations, the diffusion of NGS technologies will raise questions about their efficiency when compared to more targeted approaches, and their added value in a context of routine diagnosis.

"What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine

BACKGROUND

The growth of "big data" and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.

OBJECTIVES

To examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.

METHODS

We focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.

RESULTS

Using a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.

CONCLUSIONS

We conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.