Accounting for Capacity Constraints in Economic Evaluations of Precision Medicine: A Systematic Review

Economic Assessment in Resource-Constrained Systems: Individual-Level Simulation Model in Wet Age-Related Macular Degeneration and Diabetic Macular Oedema

INTRODUCTION

Cost-effectiveness analyses typically ignore healthcare system resource constraints. Ophthalmology is affected by resource constraints because of increasing disease prevalence and the use of resource-intensive treatments. This study evaluated the impact of resource constraints on the cost-effectiveness of faricimab 6 mg, compared with aflibercept 2 mg and ranibizumab biosimilar 0.5 mg, for treating wet age-related macular degeneration (wAMD) or diabetic macular oedema (DMO) over a 5-year horizon.

METHODS

A microsimulation model estimated the impact of resource constraints on patients visits, delays, costs and quality-adjusted life-year (QALY) losses due to treatment delays at a typical UK National Health Service eye hospital treating 1500 patients with wAMD and 500 patients with DMO. Patient characteristics, treatment regimens and treatment intervals were informed using published literature and expert opinion. Resource constraint was represented by limiting the number of available intravitreal injection appointments per week, with growing demand caused by rising disease prevalence. The model compared outcomes across three scenarios; each scenario involved treating all patients with one of the three treatments.

RESULTS

Over 5 years, in a resource-constrained hospital, compared with aflibercept, faricimab use resulted in the avoidance of 12,596 delays, saved GBP/£15,108,609 in cost and avoided the loss of 60.06 QALYs. Compared with ranibizumab biosimilar, faricimab use resulted in the avoidance of 18,910 delays, incurred £2,069,088 extra cost and avoided the loss of 105.70 QALYs, resulting in an incremental cost-effectiveness ratio of £19,574/QALY.

CONCLUSIONS

Accounting for resource constraints in health economic evaluation is crucial. Emerging therapies that are more durable and require less frequent clinic visits can reduce treatment delays, leading to improved QALY outcomes and reduced burden on healthcare systems. Faricimab reduced the number of delayed injections, leading to improved QALY outcomes for patients in a healthcare system with resource constraints. Faricimab is cost-saving when compared with aflibercept and cost-effective when compared with ranibizumab biosimilar.

Different Models, Same Results: Considerations When Choosing Between Approaches to Model Cost Effectiveness of Chimeric-Antigen Receptor T-Cell Therapy Versus Standard of Care

OBJECTIVE

Chimeric antigen-receptor T-cell therapy (CAR-T) is characterised by early phase data at the time of registration, high upfront cost and a complex manufacturing and administration process compared with standard therapies. Our objective was to compare the performance of different models to assess the cost effectiveness of CAR-T using a state-transition model (STM), partitioned survival model (PSM) and discrete event simulation (DES).

METHODS

Individual data for tisagenlecleucel for the treatment of young patients with acute lymphoblastic leukaemia (ALL) were used to populate the models. Costs and benefits were measured over a lifetime to generate a cost per quality-adjusted life-year (QALY). Model performance was compared quantitatively on the outcomes generated and a checklist developed summarising the components captured by each model type relevant to assessing cost effectiveness of CAR-T.

RESULTS

Models generated similar results with base-case analyses ranging from an incremental cost per QALY of $96,074-$99,625. DES was the only model to specifically capture CAR-T wait time, demonstrating a substantial loss of benefit of CAR-T with increased wait time.

CONCLUSION

Although model type did not meaningfully impact base-case results, the ability to incorporate an outcome-based payment arrangement (OBA) and wait time are important elements to consider when selecting a model for CAR-T. DES provided greater flexibility compared with STM and PSM approaches to deal with the complex manufacturing and administration process that can lead to extended wait times and substantially reduce the benefit of CAR-T. This is an important consideration when selecting a model type for CAR-T, so major drivers of uncertainty are considered in funding decisions.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Capturing the Impact of Constraints on the Cost-Effectiveness of Cell and Gene Therapies: A Systematic Review

OBJECTIVE

Decision-makers need to resolve constraints on delivering cell and gene therapies to patients as these treatments move into routine care. This study aimed to investigate if, and how, constraints that affect the expected cost and health consequences of cell and gene therapies have been included in published examples of cost-effectiveness analyses (CEAs).

METHOD

A systematic review identified CEAs of cell and gene therapies. Studies were identified from previous systematic reviews and by searching Medline and Embase until 21 January 2022. Constraints described qualitatively were categorised by theme and summarised by a narrative synthesis. Constraints evaluated in quantitative scenario analyses were appraised by whether they changed the decision to recommend treatment.

RESULTS

Thirty-two CEAs of cell (n = 20) and gene therapies (n = 12) were included. Twenty-one studies described constraints qualitatively (70% cell therapy CEAs; 58% gene therapy CEAs). Qualitative constraints were categorised by four themes: single payment models; long-term affordability; delivery by providers; manufacturing capability. Thirteen studies assessed constraints quantitatively (60% cell therapy CEAs; 8% gene therapy CEAs). Two types of constraint were assessed quantitatively across four jurisdictions (USA, Canada, Singapore, The Netherlands): alternatives to single payment models (n = 9 scenario analyses); improving manufacturing (n = 12 scenario analyses). The impact on decision-making was determined by whether the estimated incremental cost-effectiveness ratios crossed a relevant cost-effectiveness threshold for each jurisdiction (outcome-based payment models: n = 25 threshold comparisons made, 28% decisions changed; improving manufacturing: n = 24 threshold comparisons made, 4% decisions changed).

CONCLUSION

The net health impact of constraints is vital evidence to help decision-makers scale up the delivery of cell and gene therapies as patient volume increases and more advanced therapy medicinal products are launched. CEAs will be essential to quantify how constraints affect the cost-effectiveness of care, prioritise constraints to be resolved, and establish the value of strategies to implement cell and gene therapies by accounting for their health opportunity cost.

Economics and Precision Medicine

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders' preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future.

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

BACKGROUND

Examples of precision medicine are complex interventions featuring both testing and treatment components. Because of this complexity, there are often barriers to the introduction of such interventions. Few economic evaluations attempt to determine the impact of these barriers on the cost-effectiveness of the intervention. This study presents a case study economic evaluation that illustrates how the value of implementation methods may be used to quantify the impact of capacity constraints in a decision-analytic model.

METHODS

A baseline decision-analytic model-based economic evaluation of ALK mutation testing was reproduced from a published technology appraisal. Three constraints (commissioning awareness, localization of testing, and pathology laboratory capacity) were identified using qualitative interviews, parameterized, and incorporated into the model. Value of implementation methods were used alongside incremental cost-effectiveness ratios (ICERs) to quantify the impact on the cost-effectiveness and net monetary benefit (NMB) of each capacity constraint and from the 3 constraints combined.

RESULTS

Each of the 3 capacity constraints resulted in a loss of NMB ranging from £7773 (0.1% of the total) per year for localized testing to £4,907,893 (77%) for a lack of awareness about commissioning ALK testing. When combined, the constraints resulted in a loss of NMB of £5,289,414 (83%). The localization and limited pathology capacity constraints slightly increased the ICER, but the lack of commissioning awareness constraint did not change the ICER.

CONCLUSIONS

Capacity constraints may have a significant impact on the NMB produced by examples of precision medicine. Value of implementation methods can be used to quantify the impact of such constraints by combining the impact of the constraints on the cost-effectiveness of the intervention with the impact on the number of patients receiving the intervention.

HIGHLIGHTS

While capacity constraints may prevent the use of precision medicine in clinical practice, economic evaluations rarely account for the impact of such barriers.This study demonstrates how constraints can be identified using qualitative methods and subsequently incorporated into decision-analytic models using quantitative value of implementation methods.In addition, this article demonstrates how value of implementation methods can be used to account for the impact of capacity constraints on the costs and benefits of an intervention as well as the number of patients receiving the intervention.In the case study presented herein, a capacity constraint reducing patient access to an example of precision medicine caused the biggest loss of net monetary benefit.Health economists should consider moving beyond incremental cost-effectiveness ratios to measures of total net monetary benefit to fully capture the impact of implementing precision medicine.

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

BACKGROUND

The first histology-independent marketing authorisation in Europe was granted in 2019. This was the first time that a cancer treatment was approved based on a common biomarker rather than the location in the body at which the tumour originated. This research aims to explore the implications for National Institute for Health and Care Excellence appraisals.

METHODS

Targeted reviews were undertaken to determine the type of evidence that is likely to be available at the point of marketing authorisation and the analyses required to support National Institute for Health and Care Excellence appraisals. Several challenges were identified concerning the design and conduct of trials for histology-independent products, the greater levels of heterogeneity within the licensed population and the use of surrogate end points. We identified approaches to address these challenges by reviewing key statistical literature that focuses on the design and analysis of histology-independent trials and by undertaking a systematic review to evaluate the use of response end points as surrogate outcomes for survival end points. We developed a decision framework to help to inform approval and research policies for histology-independent products. The framework explored the uncertainties and risks associated with different approval policies, including the role of further data collection, pricing schemes and stratified decision-making.

RESULTS

We found that the potential for heterogeneity in treatment effects, across tumour types or other characteristics, is likely to be a central issue for National Institute for Health and Care Excellence appraisals. Bayesian hierarchical methods may serve as a useful vehicle to assess the level of heterogeneity across tumours and to estimate the pooled treatment effects for each tumour, which can inform whether or not the assumption of homogeneity is reasonable. Our review suggests that response end points may not be reliable surrogates for survival end points. However, a surrogate-based modelling approach, which captures all relevant uncertainty, may be preferable to the use of immature survival data. Several additional sources of heterogeneity were identified as presenting potential challenges to National Institute for Health and Care Excellence appraisal, including the cost of testing, baseline risk, quality of life and routine management costs. We concluded that a range of alternative approaches will be required to address different sources of heterogeneity to support National Institute for Health and Care Excellence appraisals. An exemplar case study was developed to illustrate the nature of the assessments that may be required.

CONCLUSIONS

Adequately designed and analysed basket studies that assess the homogeneity of outcomes and allow borrowing of information across baskets, where appropriate, are recommended. Where there is evidence of heterogeneity in treatment effects and estimates of cost-effectiveness, consideration should be given to optimised recommendations. Routine presentation of the scale of the consequences of heterogeneity and decision uncertainty may provide an important additional approach to the assessments specified in the current National Institute for Health and Care Excellence methods guide.

FURTHER RESEARCH

Further exploration of Bayesian hierarchical methods could help to inform decision-makers on whether or not there is sufficient evidence of homogeneity to support pooled analyses. Further research is also required to determine the appropriate basis for apportioning genomic testing costs where there are multiple targets and to address the challenges of uncontrolled Phase II studies, including the role and use of surrogate end points.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 76. See the NIHR Journals Library website for further project information.

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

OBJECTIVE

The objective of this study was to develop guidance contributing to improved consistency and quality in economic evaluations of personalised medicine (PM), given current ambiguity about how to measure the value of PM as well as considerable variation in the methodology and reporting in economic evaluations of PM.

METHODS

A targeted literature review of methodological papers was performed for an overview of modelling challenges in PM. Expert interviews were held to discuss best modelling practice. A systematic literature review of economic evaluations of PM was conducted to gain insight into current modelling practice. The findings were synthesised and used to develop a set of draft recommendations. The draft recommendations were discussed at a stakeholder workshop and subsequently finalised.

RESULTS

Twenty-two methodological papers were identified. Some argued that the challenges in modelling PM can be addressed within existing methodological frameworks, others disagreed. Eighteen experts were interviewed. They believed large uncertainty to be a key concern. Out of 195 economic evaluations of PM identified, 56% addressed none of the identified modelling challenges. A set of 23 recommendations was developed. Eight recommendations focus on the modelling of test-treatment pathways. The use of non-randomised controlled trial data is discouraged but several recommendations are provided in case randomised controlled trial data are unavailable. The parameterisation of structural uncertainty is recommended. Other recommendations consider perspective and discounting; premature survival data; additional value elements; patient and clinician compliance; and managed entry agreements.

CONCLUSIONS

This study provides a comprehensive list of recommendations to modellers of PM and to evaluators and reviewers of PM models.

Understanding barriers to the introduction of precision medicine in non-small cell lung cancer: a qualitative interview study

Background: While treatments targeting genetic mutations and alterations in non-small cell lung cancer (NSCLC) have been available since 2010, the adoption of such examples of precision medicine into clinical practice has historically been slow. This means that patients with NSCLC may not have received life improving and extending treatments which should have been available to them. The purpose of this qualitative interview study was to identify the barriers to the provision of examples of precision medicine for NSCLC. Methods: This study used semi-structured telephone interviews with clinicians, test providers and service commissioners to identify the perceived barriers to providing historical, current, and future examples of precision medicine in NSCLC. Participants were identified through mailing list advertisements and snowball sampling. The qualitative data was analysed using a framework analysis. Results: Interviews were conducted with 11 participants including: five oncologists; three pathologists; two clinical geneticists; and one service commissioner. A total of 17 barriers to the introduction of precision medicine for NSCLC were identified and these were grouped into five themes: the regulation of precision medicine and tests; the commissioning and reimbursement of tests and the testing process; the complexity of the logistics around providing tests; centralisation or localisation of test provision; and opinions about future developments in precision medicine for NSCLC. Conclusions: A number of barriers exist to the introduction of precision medicine in NSCLC. Addressing these barriers may improve access to novel life improving and extending treatments for patients.

Implementing Interventions with Varying Marginal Cost-Effectiveness: An Application in Precision Medicine

Purpose. A range of barriers may constrain the effective implementation of strategies to deliver precision medicine. If the marginal costs and consequences of precision medicine vary at different levels of implementation, then such variation will have an impact on relative cost-effectiveness. This study aimed to illustrate the importance and quantify the impact of varying marginal costs and benefits on the value of implementation for a case study in precision medicine. Methods. An existing method to calculate the value of implementation was adapted to allow marginal costs and consequences of introducing precision medicine into practice to vary across differing levels of implementation. This illustrative analysis used a case study based on a published decision-analytic model-based cost-effectiveness analysis of a 70-gene recurrence score (MammaPrint) for breast cancer. The impact of allowing for varying costs and benefits for the value of the precision medicine and of implementation strategies was illustrated graphically and numerically in both static and dynamic forms. Results. The increasing returns to scale exhibited by introducing this specific example of precision medicine mean that a minimum level of implementation (51%) is required for using the 70-gene recurrence score to be cost-effective at a defined threshold of €20,000 per quality-adjusted life year. The observed variation in net monetary benefit implies that the value of implementation strategies was dependent on the initial and ending levels of implementation in addition to the magnitude of the increase in patients receiving the 70-gene recurrence score. In dynamic models, incremental losses caused by low implementation accrue over time unless implementation is improved. Conclusions. Poor implementation of approaches to deliver precision medicine, identified to be cost-effective using decision-analytic model-based cost-effectiveness analysis, can have a significant economic impact on health systems. Developing and evaluating the economic impact of strategies to improve the implementation of precision medicine will potentially realize the more cost-effective use of health care budgets.