New Metrics for Economic Evaluation in the Presence of Heterogeneity: Focusing on Evaluating Policy Alternatives Rather than Treatment Alternatives

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Evaluating Policies of Expanding Versus Restricting First-Line Treatment Choices: A Cost-Effectiveness Analysis Framework

OBJECTIVES

Healthcare payers often implement coverage policies that restrict the utilization of costly new first-line treatments. Cost-effectiveness analysis can be conducted to inform these decisions by comparing the new treatment with an existing one. However, this approach may overlook important factors such as treatment effect heterogeneity and endogenous treatment selection, policy implementation costs, and diverse patient preferences across multiple treatment options. We aimed to develop a cost-effectiveness analysis framework that considers these real-world factors, facilitating the evaluation of alternative policies related to expanding or restricting first-line treatment choices.

METHODS

We introduced a metric of incremental cost-effectiveness ratio (ICER) that compares an expanded choice set (CS) including the new first-line treatment with a restricted CS excluding the new treatment. ICER(CS) accounts for treatment selection influenced by heterogeneous treatment effects and policy implementation costs. We examined a basic scenario with 2 standard first-line treatment choices and a more realistic scenario involving diverse preferences toward multiple choices. To illustrate the framework, we conducted a retrospective evaluation of including versus excluding abiraterone acetate plus prednisone (AAP) (androgen deprivation therapy [ADT] + AAP) as a first-line treatment for metastatic hormone-sensitive prostate cancer.

RESULTS

The traditional ICERs for ADT + AAP versus ADT alone and ADT+ docetaxel were $104 269 and $206 324/quality-adjusted life-year, respectively. The ICER(CS) for comparing an expanded CS with ADT + AAP with a restricted CS without ADT + AAP was $123 179/quality-adjusted life-year.

CONCLUSIONS

The proposed framework provides decision makers with policy-relevant tools, enabling them to assess the cost-effectiveness of alternative policies of expanding versus restricting patients' and physicians' first-line treatment choices.

Tools for the Economic Evaluation of Precision Medicine: A Scoping Review of Frameworks for Valuing Heterogeneity-Informed Decisions

BACKGROUND AND OBJECTIVE

Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine.

METHODS

We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine.

RESULTS

Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider.

CONCLUSIONS

Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.

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.

Can Pay-for Performance Incentive Levels be Determined Using a Cost-Effectiveness Framework?

BACKGROUND

Healthcare payers in the United States are increasingly tying provider payments to quality and value using pay-for-performance policies. Cost-effectiveness analysis quantifies value in healthcare but is not currently used to design or prioritize pay-for-performance strategies or metrics. Acute ischemic stroke care provides a useful application to demonstrate how simulation modeling can be used to determine cost-effective levels of financial incentives used in pay-for-performance policies and associated challenges with this approach.

METHODS AND RESULTS

Our framework requires a simulation model that can estimate quality-adjusted life years and costs resulting from improvements in a quality metric. A monetary level of incentives can then be back-calculated using the lifetime discounted quality-adjusted life year (which includes effectiveness of quality improvement) and cost (which includes incentive payments and cost offsets from quality improvements) outputs from the model. We applied this framework to an acute ischemic stroke microsimulation model to calculate the difference in population-level net monetary benefit (willingness-to-pay of $50 000 to $150 000/quality-adjusted life year) accrued under current Medicare policy (stroke payment not adjusted for performance) compared with various hypothetical pay-for-performance policies. Performance measurement was based on time-to-thrombolytic treatment with tPA (tissue-type plasminogen activator). Compared with current payment, equivalent population-level net monetary benefit was achieved in pay-for-performance policies with 10-minute door-to-needle time reductions (5057 more acute ischemic stroke cases/y in the 0-3-hour window) incentivized by increasing tPA payment by as much as 18% to 44% depending on willingness-to-pay for health.

CONCLUSIONS

Cost-effectiveness modeling can be used to determine the upper bound of financial incentives used in pay-for-performance policies, although currently, this approach is limited due to data requirements and modeling assumptions. For tPA payments in acute ischemic stroke, our model-based results suggest financial incentives leading to a 10-minute decrease in door-to-needle time should be implemented but not exceed 18% to 44% of current tPA payment. In general, the optimal level of financial incentives will depend on willingness-to-pay for health and other modeling assumptions around parameter uncertainty and the relationship between quality improvements and long-run quality-adjusted life expectancy and costs.

The Interaction between Price Negotiations and Heterogeneity: Implications for Economic Evaluations

Economic evaluation is an important element of the decision making process for the reimbursement of drugs. Heterogeneity can be considered an explained variation in clinical or economic outcomes based on the clinical and sociodemographic characteristics of patients. However, to our knowledge, the relationship between price negotiations and population heterogeneity has not been considered in the literature to date. If a company offers a conditional discount that is dependent on obtaining reimbursement in 2 subgroups or indications, an interaction is generated between groups that should be accounted for in economic evaluations. Critically, where the drug has 2 indications but is only cost-effective in 1 indication at the full price (herein "indication 1"), the cost savings realized from implementation of the discount in indication 1 can be used to offset the incremental cost of extending reimbursement to indication 2 at the discounted price. This reduces the incremental cost-effectiveness ratio and increases the probability of positive reimbursement compared to a stratified approach. Given the additional complexity that this introduces, we introduce a framework deemed the "hybrid approach" to guide the economic assessment. We present 2 worked examples. We show that failure to account for the interaction can lead to inaccurate conclusions regarding a drug's cost effectiveness and that adoption of strategic behavior could theoretically increase the reimbursement price of drugs. By adopting this framework, cost-effective interventions are identified that may have been previously misclassified as not being cost-effective and vice versa. Recognition of the interaction in the literature by pharmaceutical companies may influence the forms of discounts offered to decision makers. Therefore, we expect this research to have far-reaching effects on medical decision making.

Targeted Incentive Programs For Lung Cancer Screening Can Improve Population Health And Economic Efficiency

Because an intervention's clinical benefit depends on who receives it, a key to improving the efficiency of lung cancer screening with low-dose computed tomography (LDCT) is to incentivize its use among the current or former smokers who are most likely to benefit from it. Despite its clinical advantages and cost-effectiveness, only 3.9 percent of the eligible population underwent LDCT screening in 2015. Using individual lung cancer mortality risk, we developed a policy simulation model to explore the potential impact of implementing risk-targeted incentive programs, compared to either implementing untargeted incentive programs or doing nothing. We found that compared to the status quo, an untargeted incentive program that increased overall LDCT screening from 3,900 (baseline) to 10,000 per 100,000 eligible people would save 12,300 life-years and accrue a net monetary benefit (NMB) of $771 million over a lifetime horizon. Increasing screening by the same amount but targeting higher-risk people would yield an additional 2,470-6,600 life-years and an additional $210-$560 million NMB, depending on the extent of the risk-targeting. Risk-targeted incentive programs could include provider-level bonuses, health plan premium subsidies, and smoking cessation programs to maximize their impact. As clinical medicine becomes more personalized, targeting and incentivizing higher-risk people will help enhance population health and economic efficiency.