Current methodological issues in the economic assessment of personalized medicine

Scenario analysis and multi-criteria decision analysis to explore alternative reimbursement pathways for whole genome sequencing for blood cancer patients

BACKGROUND

Whole genome sequencing (WGS) has transformative potential for blood cancer management, but reimbursement is hindered by uncertain benefits relative to added costs. This study employed scenario planning and multi-criteria decision analysis (MCDA) to evaluate stakeholders' preferences for alternative reimbursement pathways, informing future health technology assessment (HTA) submission of WGS in blood cancer.

METHODS

Key factors influencing WGS reimbursement in blood cancers were identified through a literature search. Hypothetical scenarios describing various evidential characteristics of WGS for HTA were developed using the morphological approach. An online survey, incorporating MCDA weights, was designed to gather stakeholder preferences (consumers/patients, clinicians/health professionals, industry representatives, health economists, and HTA committee members) for these scenarios. The survey assessed participants' approval of WGS reimbursement for each scenario, and scenario preferences were determined using the geometric mean method, applying an algorithm to improve reliability and precision by addressing inconsistent responses.

RESULTS

Nineteen participants provided complete survey responses, primarily clinicians or health professionals (n = 6; 32 %), consumers/patients and industry representatives (both at n = 5; 26 %). "Clinical impact of WGS results on patient care" was the most critical criterion (criteria weight of 0.25), followed by "diagnostic accuracy of WGS" (0.21), "cost-effectiveness of WGS" (0.19), "availability of reimbursed treatment after WGS" (0.16), and "eligibility criteria for reimbursed treatment based on actionable WGS results" and "cost comparison of WGS" (both at 0.09). Participants preferred a scenario with substantial clinical evidence, high access to reimbursed targeted treatment, cost-effectiveness below $50,000 per quality-adjusted life year (QALY) gained, and affordability relative to standard molecular tests. Reimbursement was initially opposed until criteria such as equal cost to standard tests and better treatment accessibility were met.

CONCLUSION

Payers commonly emphasize acceptable cost-effectiveness, but strong clinical evidence for many variants and comparable costs to standard tests are likely to drive positive reimbursement decisions for WGS.

The role of economic evaluations in advancing HIV multipurpose prevention technologies in early-stage development

Product development is a high-risk undertaking, especially so when investments are prioritized for low- and middle-income countries (LMICs) where markets may be smaller, fragile, and resource-constrained. New HIV prevention technologies, such as the dapivirine vaginal ring (DVR) and long-acting injectable cabotegravir (CAB-LA), are being introduced to these markets with one indication, meeting different needs of groups such as adolescent girls and young women (AGYW) and female sex workers (FSWs) in settings with high HIV burden. However, limited supply and demand have made their uptake a challenge. Economic evaluations conducted before Phase III trials can help optimize the potential public health value proposition of products in early-stage research and development (R&D), targeting investments in the development pathway that result in products likely to be available and taken up. Public investors in the HIV prevention pipeline, in particular those focused on innovative presentations such as multipurpose prevention technologies (MPTs), can leverage early economic evaluations to understand the intrinsic uncertainty in market characterization. In this perspective piece, we reflect on the role of economic evaluations in early product development and on methodological considerations that are central to these analyses. We also discuss methods, in quantitative and qualitative research that can be deployed in early economic evaluations to address uncertainty, with examples applied to the development of future technologies for HIV prevention and MPTs.

Health Economic Evidence and Modeling Challenges for Liquid Biopsy Assays in Cancer Management: A Systematic Literature Review

BACKGROUND

Cancer-derived material circulating in the bloodstream and other bodily fluids, referred to as liquid biopsies (LBs), has become an appealing adjunct or alternative to tissue biopsies, showing vital promise in several clinical applications.

PURPOSE

A systematic literature review was conducted to (1) summarize the current health economic evidence for LB assays and (2) identify and analyze the studies addressed or reported on the challenges of health economic modeling in precision medicine.

METHODS

Relevant studies were identified in the EMBASE, MEDLINE, Cochrane Library, EconLit, and the University of Melbourne Full Text Journal databases from 1 January 2013 to 16 September 2022. Included papers were selected if they were economic evaluations and/or budget impact analyses.

RESULTS

A total of 24 studies were included and analyzed, with the majority being full economic evaluations (n = 19, 79.2%). Four studies (16.7%) were health and budget impact analyses, and one study (4.1%) incorporated both an economic evaluation and a budget impact analysis. Cohort-level modeling techniques were the most common approach (n = 16; 80%). LB technologies were cost-effective in 15 studies (75%) considering different biomarkers, cancer types and stages, and economic analyses. These studies evaluated LBs for screening and early detection (66.7%), treatment selection (26.7%), and monitoring treatment response (6.6%). Budget impact analysis results were varied among included studies, with the majority of studies (n = 4; 80%) reporting either cost savings, minimal, or modest budget impact, while one study (20%) reported LBs as an efficient strategy. The reviewed studies often inadequately reported or addressed modeling challenges, such as patient-level processes, the combination of tests and treatments, preferences, and uncertainty.

CONCLUSION

LBs could provide a cost-effective approach for treatment selection in lung cancer and aid in the screening and early detection of other cancers, including colorectal, gastric, breast, and brain cancers. This is in comparison with various alternatives, such as the standard of care (SOC) and no screening scenario. However, it is important to mention that in some comparisons, LBs were used in combination with SOC instead of replacing it. Importantly, few studies have pointed toward LBs' cost-effectiveness for monitoring treatment response. Most health and budget impact analyses, especially those focused on lung cancer, suggest potential cost savings or a minimal-to-moderate budget impact. Nevertheless, additional research is needed to ascertain their effectiveness across various stages of lung and colorectal cancer, as well as to address potential modeling challenges.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022307939.

Evaluating the Value for Money of Precision Medicine from Early Cycle to Market Access: A Comprehensive Review of Approaches and Challenges

OBJECTIVES

This study aimed to perform a comprehensive review of modeling approaches and methodological and policy challenges in the economic evaluation (EE) of precision medicine (PM) across clinical stages.

METHODS

First, a systematic review was performed to assess the approaches of EEs in the past 10 years. Next, a targeted review of methodological articles was conducted for methodological and policy challenges in performing EEs of PM. All findings were synthesized into a structured framework that focused on patient population, Intervention, Comparator, Outcome, Time, Equity and ethics, Adaptability and Modeling aspects, named the "PICOTEAM" framework. Finally, a stakeholder consultation was conducted to understand the major determinants of decision making in PM investment.

RESULTS

In 39 methodological articles, we identified major challenges to the EE of PM. These challenges include that PM applications involve complex and evolving clinical decision space, that clinical evidence is sparse because of small subgroups and complex pathways in PM settings, a one-time PM application may have lifetime or intergenerational impacts but long-term evidence is often unavailable, and that equity and ethics concerns are exceptional. In 275 EEs of PM, current approaches did not sufficiently capture the value of PM compared with targeted therapies, nor did they differentiate Early EEs from Conventional EEs. Finally, policy makers perceived the budget impact, cost savings, and cost-effectiveness of PM as the most important determinants in decision making.

CONCLUSIONS

There is an urgent need to modify existing guidelines or develop a new reference case that fits into the new healthcare paradigm of PM to guide decision making in research and development and market access.

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.

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.

How are Companion Diagnostics Considered in Economic Evaluations of Oncology Treatments? A Review of Health Technology Assessments

BACKGROUND

Companion diagnostic (CDx) testing is increasingly used to identify eligible patients for targeted treatments. Guidance on how CDx testing should be incorporated into cost-effectiveness models (CEM) is limited. This review evaluated how health technology assessment bodies and research organizations considered CDx in CEMs of targeted therapies in oncology and whether this ultimately impacted their decisions or time from regulatory approval to recommendations.

METHODS

An exhaustive list of approved CDx tests in oncology was compiled. For corresponding indications and treatments, NICE appraisals published between 2016 and 2019 were identified. Then, assessments for the same treatments issued from 11 other agencies were reviewed. Data extracted included background and CDx information, CDx's role in the CEM, and recommendations.

RESULTS

Twenty-seven NICE appraisals were identified; 15 considered CDx testing in the CEM, while 12 did not, mainly because testing had already been established for the comparators within the same class or in clinical practice from a prior treatment line. Both testing costs and mutation prevalence drove CDx testing costs per patient. The cross-comparison of assessments showed that CDx test characteristics were inconsistently reported. Time from regulatory approval to recommendations was not impacted by CDx cost inclusion in CEMs.

CONCLUSION

CDx testing was included in cost-effectiveness models whenever mutation testing was required solely for patients receiving targeted treatment; cost per patient was based on test costs and mutation prevalence. It is unclear if expanded reliance on CDx testing will impact future assessments of targeted therapies. A future update is warranted to track trends over time.

Accounting for Intergenerational Cascade Testing in Economic Evaluations of Clinical Genomics: A Scoping Review

OBJECTIVES

Clinical genomics is emerging as a diagnostic tool in the identification of blood relatives at risk of developing heritable diseases. Our objective was to identify how genetic cascade screening has been incorporated into health economic evaluations.

METHODS

A scoping review was conducted to identify how multiple generations of a family were included in economic evaluations of clinical genomic sequencing, how many and which relatives were included, and uptake rates. Databases were searched for full economic evaluations of genetic interventions that screened multiple generations of families and were in English language, and no restrictions were made for disease or publication type. Data were synthesized using a narrative approach.

RESULTS

Twenty-five studies were included covering a range of diseases in various countries. Markov cohort models were mostly used with hypothetical populations and unsupported by clinical evidence. Cascade testing was either the primary intervention or secondary to the index cases. The number and type of relatives were based on assumptions or identified through population or family records, clinical registry data, or clinical literature. Studies included only immediate family members and the uptake of testing ranged between 20% and 100%. All interventions were reported as cost-effective, and a higher number of relatives was a key driver.

CONCLUSIONS

Several economic evaluations have considered the impacts of cascade testing interventions within clinical genomics. Ideally, models supported with high-quality clinical data are needed and, in their absence, transparent and justifiable assumptions of uptake rates and choices about including relatives. Consideration of more appropriate modeling types is required.

Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review

Objectives

Molecular biomarker tests can inform the clinical management of genomic heterogeneous hematological malignancies, yet their availability in routine care largely depends on the supporting health economic evidence. This study aims to systematically review the economic evidence for recent molecular biomarker tests in hematological malignancies.

Methods

We conducted a systematic search in five electronic databases for studies published between January 2010 and October 2020. Publications were independently screened by two reviewers. Clinical study characteristics, economic methodology, and results were extracted, and reporting quality was assessed.

Results

Fourteen studies were identified, of which half (n = 7; 50%) were full economic evaluations examining both health and economic outcomes. Studies were predominantly conducted in a first‐line treatment setting (n = 7; 50%) and adopted a non‐lifetime time horizon to measure health outcomes and costs (n = 7; 50%). Five studies reported that companion diagnostics for associated therapies were likely cost‐effective for acute myeloid leukemia, chronic myeloid leukemia, diffuse large B‐cell lymphoma, and multiple myeloma. Four studies suggested molecular biomarker tests for treatment monitoring in chronic myeloid leukemia were likely cost‐saving.

Conclusions

Although there is initial confirmation of the promising health economic results, the present research for molecular biomarker tests in hematological malignancies is sparse with many applications of technological advances yet to be evaluated.

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.

Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer

OBJECTIVES

This study aimed to provide detailed guidance on modeling approaches for implementing competing events in discrete event simulations based on censored individual patient data (IPD).

METHODS

The event-specific distributions (ESDs) approach sampled times from event-specific time-to-event distributions and simulated the first event to occur. The unimodal distribution and regression approach sampled a time from a combined unimodal time-to-event distribution, representing all events, and used a (multinomial) logistic regression model to select the event to be simulated. A simulation study assessed performance in terms of relative absolute event incidence difference and relative entropy of time-to-event distributions for different types and levels of right censoring, numbers of events, distribution overlap, and sample sizes. Differences in cost-effectiveness estimates were illustrated in a colorectal cancer case study.

RESULTS

Increased levels of censoring negatively affected the modeling approaches' performance. A lower number of competing events and higher overlap of distributions improved performance. When IPD were censored at random times, ESD performed best. When censoring occurred owing to a maximum follow-up time for 2 events, ESD performed better for a low level of censoring (ie, 10%). For 3 or 4 competing events, ESD better represented the probabilities of events, whereas unimodal distribution and regression better represented the time to events. Differences in cost-effectiveness estimates, both compared with no censoring and between approaches, increased with increasing censoring levels.

CONCLUSIONS

Modelers should be aware of the different modeling approaches available and that selection between approaches may be informed by data characteristics. Performing and reporting extensive validation efforts remains essential to ensure IPD are appropriately represented.

Conceptualising 'Benefits Beyond Health' in the Context of the Quality-Adjusted Life-Year: A Critical Interpretive Synthesis

There is growing interest in extending the evaluative space of the quality-adjusted life-year framework beyond health. Using a critical interpretive synthesis approach, the objective was to review peer-reviewed literature that has discussed non-health outcomes within the context of quality-adjusted life-years and synthesise information into a thematic framework. Papers were identified through searches conducted in Web of Science, using forward citation searching. A critical interpretive synthesis allows for the development of interpretations (synthetic constructs) that go beyond those offered in the original sources. The final output of a critical interpretive synthesis is the synthesising argument, which integrates evidence from across studies into a coherent thematic framework. A concept map was developed to show the relationships between different types of non-health benefits. The critical interpretive synthesis was based on 99 papers. The thematic framework was constructed around four themes: (1) benefits affecting well-being (subjective well-being, psychological well-being, capability and empowerment); (2) benefits derived from the process of healthcare delivery; (3) benefits beyond the recipient of care (spillover effects, externalities, option value and distributional benefits); and (4) benefits beyond the healthcare sector. There is a wealth of research concerning non-health benefits and the evaluative space of the quality-adjusted life-year. Further dialogue and debate are necessary to address conceptual and normative challenges, to explore the societal willingness to sacrifice health for benefits beyond health and to consider the equity implications of different courses of action.

A Practical Guide to Modeling and Conducting a Cost-Effectiveness Analysis of Companion Biomarker Tests for Targeted Therapies Using R: Tutorial Paper

Despite the increasing number of potential biomarkers identified in laboratories and reported in much literature, the adoption of biomarkers routinely available in clinical practice to inform treatment decisions is very limited. Reimbursement decisions for new health technologies are often informed by economic evaluations; however, economic evaluations of diagnostics/testing technologies, such as companion biomarker tests, are far less frequently reported than drugs. Furthermore, few countries provide the health economic evaluation methods guide specific to co-dependent technologies such as companion diagnostics or precision medicines. Therefore, this paper aims to guide the process of the development of cost-effectiveness models of cancer biomarkers for targeted therapies, focusing on companion diagnostics. This tutorial paper provides practical guidance on how to conduct economic evaluations of cancer biomarkers and how to model the characteristics of the biomarker tests as part of the value for money of corresponding targeted therapies. This paper presents a brief introduction to the methods and data requirements, a step-by-step guide to constructing a health economic model of companion cancer biomarkers, and a discussion of issues that arise in their application to healthcare decision making. This practical guidance is provided in R, and worked examples are provided in this paper with R codes in the accompanying electronic supplementary material.

How are we evaluating the cost-effectiveness of companion biomarkers for targeted cancer therapies? A systematic review

Background

Despite the increasing economic assessment of biomarker-guided therapies, no clear agreement exists whether existing methods are sufficient or whether different methods might produce different cost-effectiveness results. This study aims to examine current practices of modeling companion biomarkers when assessing the cost-effectiveness of targeted cancer therapies. It investigates the current methods in modeling the characteristics of companion diagnostics based on existing economic evaluations of biomarker-guided therapies in cancer.

Methods

A literature search was performed using Medline, Embase, EconLit, Cochrane library for economic evaluations of biomarker-guided therapies with companion diagnostics in cancer. Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies were selected using pre-specified eligibility criteria based on the PICO framework. To make the included studies more comparable, we qualitatively synthesized the data under nine domains of methods where consensus was deemed lacking.

Results

Only four of the twenty-two studies included in this review were found to be of good quality with respect to incorporating the characteristics of companion biomarkers in economic evaluations. However, many evaluations focused on a pre-selected patient group rather than including all patients regardless of their biomarker status. Companion biomarker characteristics captured in evaluations were often limited to the cost or the accuracy of the test. Often, only the costs of biomarker testing were modelled. Clinical outcomes and health state utilities were often not included due to the limited data generated by clinical trials. Methods of economic evaluation were not applied consistently in assessments of companion cancer biomarkers for targeted therapies. It was also shown that conflicting cost-effectiveness results were likely depending on what comparator arm was chosen and what comparison structure was designed in the model.

Conclusion

We found no consistent approach applied in assessing the value of companion biomarker tests and including the characteristics of biomarkers in an economic evaluation of targeted oncology therapies. Currently, many economic evaluations fail to capture the full value of companion biomarkers beyond sensitivity/specificity and cost related to biomarker testing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08725-4.

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.

Health Economic Evidence of Point-of-Care Testing: A Systematic Review

OBJECTIVE

Point-of-care testing (POCT) has become an essential diagnostic technology for optimal patient care. Its implementation, however, still falls behind. This paper reviews the available evidence on the health economic impact of introducing POCT to assess if poor POCT uptake may be related to lacking evidence.

STUDY DESIGN

The Scopus and PubMed databases were searched to identify publications describing a health economic evaluation of a point-of-care (POC) test. Data were extracted from the included publications, including general and methodological characteristics as well as the study results summarized in either cost, effects or an incremental cost-effectiveness ratio. Results were sorted into six groups according to the POC test's purpose (diagnosis, screening or monitoring) and care setting (primary care or secondary care). The reporting quality of the publications was determined using the CHEERS checklist.

RESULTS

The initial search resulted in 396 publications, of which 44 met the inclusion criteria. Most of the evaluations were performed in a primary care setting (n = 31; 70.5%) compared with a secondary care setting (n = 13; 29.5%). About two thirds of the evaluations were on POC tests implemented with a diagnostic purpose (n = 28; 63.6%). More than 75% of evaluations concluded that POCT is recommended for implementation, although in some cases only under specific circumstances and conditions. Compliance with the CHEERS checklist items ranged from 20.8% to 100%, with an average reporting quality of 72.0%.

CONCLUSION

There were very few evaluations in this review that advised against the implementation of POCT. However, the uptake of POCT in many countries remains low. Even though the evaluations included in this review did not always include the full long-term benefits of POCT, it is clear that health economic evidence across a few dimensions of value already indicate the benefits of POCT. This suggests that the lack of evidence on POCT is not the primary barrier to its implementation and that the low uptake of these tests in clinical practice is due to (a combination of) other barriers. In this context, aspects around organization of care, support of clinicians and quality management may be crucial in the widespread implementation of POCT.

Implementation of personalized medicine in a context of moral hazard and uncertainty about treatment efficacy

This paper analyzes the decision of a health authority to implement personalized medicine. We consider a model in which the health authority has three possibilities. It can apply either the same treatment (a standard or a new treatment) to the whole population or implement personalized medicine, i.e., use genetic information to offer the most suitable treatment to each patient. We first characterize the drug reimbursement contract of a firm producing a new treatment with a companion genetic test when the firm can undertake an effort to improve drug quality. Then, we determine the conditions under which personalized medicine should be implemented when this effort is observable and when it is not. Finally, we show how the unobservability of effort affects the conditions under which the health authority implements personalized medicine.

Being Precise About Precision Medicine: What Should Value Frameworks Incorporate to Address Precision Medicine? A Report of the Personalized Precision Medicine Special Interest Group

Precision medicine is a dynamic area embracing a diverse and increasing type of approaches that allow the targeting of new medicines, screening programs or preventive healthcare strategies, which include the use of biologic markers or complex tests driven by algorithms also potentially taking account of patient preferences. The International Society for Pharmacoeconomics and Outcome Research expanded its current work around precision medicine to (1) describe the evolving paradigm of precision medicine with examples of current and evolving applications, (2) describe key stakeholders perspectives on the value of precision medicine in their respective domains, and (3) define the core factors that should be considered in a value assessment framework for precision medicine. With the ultimate goal of improving health of well-defined patient groups, precision medicine will affect all stakeholders in the healthcare system at multiple levels spanning the individual perspective to the societal perspective. For an efficient, timely and practical precision medicine value assessment framework, it will be important to address these multiple perspectives through building consensus among the stakeholders for robust procedures and measures of value aspects, including performance of precision mechanism; aligned reimbursement processes of precision mechanism and subsequent treatment; transparent expectations for evidence requirements and study designs adequately matched to the intended use of the precision mechanism and to the smaller target patient populations; recognizing the potential range of value-generation such as ruling-in and ruling-out decisions.

User-Centered Virtual Reality for Promoting Relaxation: An Innovative Approach

Virtual reality has been used effectively to promote relaxation and reduce stress. It is possible to find two main approaches to achieve such aims across the literature. The first one is focused on generic environments filled with relaxing "narratives" to induce control over one's own body and physiological response, while the second one engages the user in virtual reality-mediated activities to empower his/her own abilities to regulate emotion. The scope of the present contribution is to extend the discourse on VR use to promote relaxation, by proposing a third approach. This would be based on VR with personalized content, based on user research to identify important life events. As a second step, distinctive features of such events may be rendered with symbols, activities or other virtual environments contents. According to literature, it is possible that such an approach would obtain more sophisticated and long-lasting relaxation in users. The present contribution explores this innovative theoretical proposal and its potential applications within future research and interventions.

The Cost-Effectiveness of Multigene Panel Testing for Hereditary Breast and Ovarian Cancer in Norway

Background. Expansion of routine genetic testing for hereditary breast and ovarian cancer from conventional BRCA testing to a multigene test could improve diagnostic yield and increase the opportunity for cancer prevention in both identified carriers and their relatives. We use an economic decision model to assess whether the current knowledge on non-BRCA mutation prevalence, cancer risk, and patient preferences justifies switching to a multigene panel for testing of early-onset breast cancer patients. Methods. We evaluated routine testing by BRCA testing, a 7-gene panel, and a 14-gene panel using individual-level simulations of annual health state transitions over a lifetime perspective. Breast and ovarian cancer incidence is reduced and posttreatment survival is improved when high-risk mutations are detected and risk-reducing treatment offered. Most model inputs were synthesized from published literature. Intermediate health outcomes included breast and ovarian cancer incidence rates, along with organ-specific cancer mortality. Cost-effectiveness outcomes were health sector costs and quality-adjusted life years. Results. Intermediate health outcomes improved by testing with multigene panels. At a cost-effectiveness threshold of $77,000, a 7-gene panel test with five non-BRCA genes was the optimal strategy with an incremental cost-effectiveness ratio of $53,310 per quality-adjusted life year compared to BRCA-only testing. Limitations. Unable to stratify carriers to specific mutations within genes, we can only make predictions on the gene level, with combined risk estimates for known variants. As mutation prevalence is the absolute upper bound of returns to more expansive testing, the rarity of modelled mutations makes analysis outcomes sensitive to model implementation. Conclusions. A 7-gene panel to diagnose hereditary breast and ovarian cancer in early-onset breast cancer patients can be a cost-effective alternative to current BRCA-only testing in Norway.

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.

Health economic impact of liquid biopsies in cancer management

INTRODUCTION

Liquid biopsies (LBs) are referred to as the sampling and analysis of non-solid tissue, primarily blood, as a diagnostic and monitoring tool for cancer. Because LBs are largely non-invasive, they are a less-costly alternative for serial analysis of tumor progression and heterogeneity to facilitate clinical management. Although a variety of tumor markers are proposed (e.g., free-circulating DNA), the clinical evidence for Circulating Tumor Cells (CTCs) is currently the most developed. Areas covered: This paper presents a health economic perspective of LBs in cancer management. We first briefly introduce the requirements in biomarker development and validation, illustrated for CTCs. Second, we discuss the state-of-art on the clinical utility of LBs in breast cancer in more detail. We conclude with a future perspective on the clinical use and reimbursement of LBs Expert commentary: A significant increase in clinical research on LBs can be observed and the results suggest a rapid change of cancer management. In addition to studies evaluating clinical utility of LBs, a smooth translation into clinical practice requires systematic assessment of the health economic benefits. This paper argues that (early stage) health economic research is required to facilitate its clinical use and to prioritize further evidence development.

Personalized Medicine and Pay for Performance: Should Pharmaceutical Firms be Fully Penalized when Treatment Fails?

In this article, we model the behavior of a pharmaceutical firm that has marketing authorization for a new therapy believed to be a candidate for personalized use in a subset of patients, but that lacks information as to why a response is seen only in some patients. We characterize the optimal outcome-based reimbursement policy a health authority should follow to encourage the pharmaceutical firm to undertake research and development activities to generate the information needed to effectively stratify patients. Consistent with the literature, we find that for a pharmaceutical firm that does not undertake research and development activities, when the treatment fails, the total price of the drug must be returned to the healthcare system (full penalization). By contrast, if the firm undertakes research and development activities that make the implementation of personalized medicine possible, treatment failure should not be fully penalized. Surprisingly, in some cases, particularly for high-efficacy drugs and small target populations, the optimal policy may not require any penalty for treatment failure. To illustrate the main results of the analysis, we provide a numerical simulation and a graphical analysis.

Cost-effectiveness analyses of genetic and genomic diagnostic tests

Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the chance to diagnose rare inherited diseases or inform the targeting of therapies. New genomic diagnostic tests compete with traditional approaches to diagnosis, including the genetic testing of single genes and other clinical strategies, for finite health-care budgets. In this context, decision analytic model-based cost-effectiveness analysis is a useful method to help evaluate the costs versus consequences of introducing new health-care interventions. This Perspective presents key methodological, technical, practical and organizational challenges that must be considered by decision-makers responsible for the allocation of health-care resources to obtain robust and timely information about the relative cost-effectiveness of the increasing numbers of emerging genomic tests.

The economic case for precision medicine

Introduction: The advancement of precision medicine into routine clinical practice has been highlighted as an agenda for national and international health care policy. A principle barrier to this advancement is in meeting requirements of the payer or reimbursement agency for health care. This special report aims to explain the economic case for precision medicine, by accounting for the explicit objectives defined by decision-makers responsible for the allocation of limited health care resources. Areas covered: The framework of cost-effectiveness analysis, a method of economic evaluation, is used to describe how precision medicine can, in theory, exploit identifiable patient-level heterogeneity to improve population health outcomes and the relative cost-effectiveness of health care. Four case studies are used to illustrate potential challenges when demonstrating the economic case for a precision medicine in practice. Expert commentary: The economic case for a precision medicine should be considered at an early stage during its research and development phase. Clinical and economic evidence can be generated iteratively and should be in alignment with the objectives and requirements of decision-makers. Programmes of further research, to demonstrate the economic case of a precision medicine, can be prioritized by the extent that they reduce the uncertainty expressed by decision-makers.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models

Background

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.

Methods

Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.

Results

Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.

Conclusions

Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12874-017-0437-y) contains supplementary material, which is available to authorized users.

Simulation modeling for stratified breast cancer screening - a systematic review of cost and quality of life assumptions

BACKGROUND

The economic evaluation of stratified breast cancer screening gains momentum, but produces also very diverse results. Systematic reviews so far focused on modeling techniques and epidemiologic assumptions. However, cost and utility parameters received only little attention. This systematic review assesses simulation models for stratified breast cancer screening based on their cost and utility parameters in each phase of breast cancer screening and care.

METHODS

A literature review was conducted to compare economic evaluations with simulation models of personalized breast cancer screening. Study quality was assessed using reporting guidelines. Cost and utility inputs were extracted, standardized and structured using a care delivery framework. Studies were then clustered according to their study aim and parameters were compared within the clusters.

RESULTS

Eighteen studies were identified within three study clusters. Reporting quality was very diverse in all three clusters. Only two studies in cluster 1, four studies in cluster 2 and one study in cluster 3 scored high in the quality appraisal. In addition to the quality appraisal, this review assessed if the simulation models were consistent in integrating all relevant phases of care, if utility parameters were consistent and methodological sound and if cost were compatible and consistent in the actual parameters used for screening, diagnostic work up and treatment. Of 18 studies, only three studies did not show signs of potential bias.

CONCLUSION

This systematic review shows that a closer look into the cost and utility parameter can help to identify potential bias. Future simulation models should focus on integrating all relevant phases of care, using methodologically sound utility parameters and avoiding inconsistent cost parameters.

Cost Effectiveness of Candida Polymerase Chain Reaction Detection and Empirical Antifungal Treatment among Patients with Suspected Fungal Peritonitis in the Intensive Care Unit

BACKGROUND

Mortality from intra-abdominal candidiasis in intensive care units (ICUs) is high. It takes many days for peritoneal-fluid fungal culture to become positive, and the recommended empirical antifungal therapy involves excessive costs. Polymerase chain reaction (PCR) should produce results more rapidly than fungal culture.

OBJECTIVES

To perform a cost-effectiveness analysis of the combination of several diagnostic and therapeutic strategies to manage Candida peritonitis in non-neutropenic adult patients in ICUs.

METHODS

We constructed a decision tree model to evaluate the cost effectiveness. Cost and effectiveness were taken into account in a 1-year time horizon and from the French National Health Insurance perspective. Six strategies were compared: fluconazole or echinocandin as an empirical therapy, plus diagnosis by fungal culture or detection by PCR of all Candida species, or use of PCR to detect most fluconazole-resistant Candida species (i.e., Candida krusei and Candida glabrata).

RESULTS

The use of fluconazole empirical treatment and PCR to detect all Candida species is more cost effective than using fluconazole empirical treatment without PCR (incremental cost-effectiveness ratio of €40,055/quality-adjusted life-year). Empirical treatment with echinocandin plus PCR to detect C. krusei and C. glabrata is the most effective strategy, but has an incremental cost-effectiveness ratio of €93,776/quality-adjusted life-year. If the cost of echinocandin decreases, then strategies involving PCR plus empirical echinocandin become more cost-effective.

CONCLUSIONS

Detection by PCR of all Candida species and of most fluconazole-resistant Candida species could improve the cost-effectiveness of fluconazole and echinocandin given to non-neutropenic patients with suspected peritoneal candidiasis in ICUs.

Comparison of Timed Automata with Discrete Event Simulation for Modeling of Biomarker-Based Treatment Decisions: An Illustration for Metastatic Castration-Resistant Prostate Cancer

BACKGROUND

With the advent of personalized medicine, the field of health economic modeling is being challenged and the use of patient-level dynamic modeling techniques might be required.

OBJECTIVES

To illustrate the usability of two such techniques, timed automata (TA) and discrete event simulation (DES), for modeling personalized treatment decisions.

METHODS

An early health technology assessment on the use of circulating tumor cells, compared with prostate-specific antigen and bone scintigraphy, to inform treatment decisions in metastatic castration-resistant prostate cancer was performed. Both modeling techniques were assessed quantitatively, in terms of intermediate outcomes (e.g., overtreatment) and health economic outcomes (e.g., net monetary benefit). Qualitatively, among others, model structure, agent interactions, data management (i.e., importing and exporting data), and model transparency were assessed.

RESULTS

Both models yielded realistic and similar intermediate and health economic outcomes. Overtreatment was reduced by 6.99 and 7.02 weeks by applying circulating tumor cell as a response marker at a net monetary benefit of -€1033 and -€1104 for the TA model and the DES model, respectively. Software-specific differences were observed regarding data management features and the support for statistical distributions, which were considered better for the DES software. Regarding method-specific differences, interactions were modeled more straightforward using TA, benefiting from its compositional model structure.

CONCLUSIONS

Both techniques prove suitable for modeling personalized treatment decisions, although DES would be preferred given the current software-specific limitations of TA. When these limitations are resolved, TA would be an interesting modeling alternative if interactions are key or its compositional structure is useful to manage multi-agent complex problems.

Using Cost-Effectiveness Analysis to Quantify the Value of Genomic-Based Diagnostic Tests: Recommendations for Practice and Research

AIMS

New sequencing technologies allow increased opportunities to use genomic-based diagnostic tests (genomic tests) in routine clinical practice, which will impact healthcare budgets and patients' outcomes. This article aims to generate a list of recommendations on how the principles and methods of cost-effectiveness analysis (CEA) can be used to quantify the costs and benefits of genomic tests.

METHODS

A systematic literature search identified publications describing the use of CEA to evaluate genomic tests. Data were extracted as key concepts to produce a thematic list of previously described challenges and solutions to using CEA to evaluate genomic tests. Defining features of evaluating genomic tests were categorized into a list of key recommendations for applying methods in practice and for research needs.

RESULTS

Features producing challenges in the implementation of CEA to evaluate genomic tests were as follows: the ability of the tests to diagnose multiple disorders; potential consequences for future generations suggesting an infinite time horizon; and the potential need to consider nonhealth benefits.

CONCLUSIONS

CEA was identified as an appropriate evaluative framework for genomic tests, although standard methods may need modification and important method research questions remain. Key recommendations suggest a need for research to reflect: sharing genomic information across generations; genomic tests for multiple disorders; and health and nonhealth benefits.

Toward health technology assessment of whole-genome sequencing diagnostic tests: challenges and solutions

Whole-genome sequencing (WGS) is being applied within research settings across Europe to develop genomic WGS-based diagnostic tests. The focus of this perspective paper is to describe if, and how, current approaches of health technology assessment could be applied to WGS-based diagnostic tests. This perspective draws on the collective view from a trans-European multidisciplinary consortium of methodologists, clinicians and scientists. Specific challenges can be described by using the PICO (population, intervention, comparator, outcome) framework to inform health technology assessment. Practical solutions are suggested which require joined-up, multidisciplinary working across healthcare systems using existing expert networks so that emergent issues for the health technology assessment of WGS can be met in a timely fashion.

"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.

A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

INTRODUCTION

The ongoing development of genomic medicine and the use of molecular and imaging markers in personalized medicine (PM) has arguably challenged the field of health economic modeling (HEM). This study aims to provide detailed insights into the current status of HEM in PM, in order to identify if and how modeling methods are used to address the challenges described in literature. Areas covered: A review was performed on studies that simulate health economic outcomes for personalized clinical pathways. Decision tree modeling and Markov modeling were the most observed methods. Not all identified challenges were frequently found, challenges regarding companion diagnostics, diagnostic performance, and evidence gaps were most often found. However, the extent to which challenges were addressed varied considerably between studies. Expert commentary: Challenges for HEM in PM are not yet routinely addressed which may indicate that either (1) their impact is less severe than expected, (2) they are hard to address and therefore not managed appropriately, or (3) HEM in PM is still in an early stage. As evidence on the impact of these challenges is still lacking, we believe that more concrete examples are needed to illustrate the identified challenges and to demonstrate methods to handle them.

Implementing personalized medicine with asymmetric information on prevalence rates

Although personalized medicine is becoming the new paradigm to manage some diseases, the economics of personalized medicine have only focused on assessing the efficiency of specific treatments, lacking a theoretical framework analyzing the interactions between pharmaceutical firms and healthcare systems leading to the implementation of personalized treatments. We model the interaction between the hospitals and the manufacturer of a new treatment as an adverse selection problem where the firm does not have perfect information on the prevalence across hospitals of the genetic characteristics of the patients making them eligible to receive a new treatment. As a result of the model, hospitals with high prevalence rates benefit from the information asymmetry only when the standard treatment is inefficient when applied to the patients eligible to receive the new treatment. Otherwise, information asymmetry has no value. Personalized medicine may be fully or partially implemented depending on the proportion of high prevalence hospitals.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Assessment of pharmacogenetic tests: presenting measures of clinical validity and potential population impact in association studies

The progressing discovery of genetic variants associated with drug-related adverse events has raised expectations for pharmacogenetic tests to improve drug efficacy and safety. To further the use of pharmacogenetics in health care, tests with sufficient potential to improve efficacy and safety, as reflected by good clinical validity and population impact, need to be identified. The potential benefit of pharmacogenetic tests is often concluded from the strength of the association between the variant and the adverse event; measures of clinical validity are generally not reported. This paper describes measures of clinical validity and potential population health impact that can be calculated from association studies. We explain how these measures are influenced by the strength of the association and by the frequencies of the variant and the adverse event. The measures are illustrated using examples of testing for HLA-B*5701 associated with abacavir-induced hypersensitivity and SLCO1B1 c.521T>C (*5) associated with simvastatin-induced adverse events.

Economic Evaluation in Stratified Medicine: Methodological Issues and Challenges

BACKGROUND

Stratified Medicine (SM) is becoming a practical reality with the targeting of medicines by using a biomarker or genetic-based diagnostic to identify the eligible patient sub-population. Like any healthcare intervention, SM interventions have costs and consequences that must be considered by reimbursement authorities with limited resources. Methodological standards and guidelines exist for economic evaluations in clinical pharmacology and are an important component for health technology assessments (HTAs) in many countries. However, these guidelines have initially been developed for traditional pharmaceuticals and not for complex interventions with multiple components. This raises the issue as to whether these guidelines are adequate to SM interventions or whether new specific guidance and methodology is needed to avoid inconsistencies and contradictory findings when assessing economic value in SM.

OBJECTIVE

This article describes specific methodological challenges when conducting health economic (HE) evaluations for SM interventions and outlines potential modifications necessary to existing evaluation guidelines /principles that would promote consistent economic evaluations for SM.

RESULTS/CONCLUSIONS

Specific methodological aspects for SM comprise considerations on the choice of comparator, measuring effectiveness and outcomes, appropriate modeling structure and the scope of sensitivity analyses. Although current HE methodology can be applied for SM, greater complexity requires further methodology development and modifications in the guidelines.

A Systematic Review of Health Economic Evaluations of Diagnostic Biomarkers

BACKGROUND

Diagnostic biomarkers have multiple applications along the care process and have a large potential in optimizing treatment decisions. However, many diagnostic biomarkers struggle to gain market access and obtain appropriate coverage because of a lack of evidence on their health economic impact.

OBJECTIVES

The aim was to review the (methodological) characteristics of recent economic evaluations on diagnostic biomarkers and examine whether these studies dealt with specific issues such as different payer perspectives, preference heterogeneity, and multiple applications in subpopulations.

METHODS

The PubMed database and the National Health Service Economic Evaluation Database were searched. Full economic evaluations published after 2009 assessing diagnostic biomarkers for the main non-communicable diseases in middle-income or high-income countries were considered eligible. Empirical and methodological study characteristics were summarized, as was the handling of specific issues related to the economic evaluation of personalized medicine.

RESULTS

Thirty-three economic evaluations were included, of which 25 were model-based analyses. The number of strategies compared ranged from two to 17 per study, and was especially large in studies assessing genetic testing in patients and their relatives. Cost-effectiveness results were most sensitive to test accuracy and costs of the biomarker (N = 7), the relative risk of an event (N = 4), and the proportion of people accepting genetic testing (N = 2). One study incorporated patient preferences, and none of the studies considered different payer perspectives, cost sharing arrangements or variable opportunity costs due to population density variability.

CONCLUSIONS

Published health economic evaluations of biomarkers used for diagnosing, staging diseases, and guiding treatment selection are characterized by a large number of comparators to model the potential clinical applications and to determine their value. Assessing outcomes beyond health as well as specific issues, such as different payer perspectives and patient preferences, is crucial to fully capture the potential health economic impact of diagnostic biomarkers and to inform value-based reimbursement.

Can genomic medicine improve financial sustainability of health systems?

Recent years have seen increased use of genomic technologies in a variety of research and clinical settings. Genomic medicine is not a cost-containment measure per se, but is viewed as having the potential to bend the healthcare cost curve. Currently, it is unknown how systematic adoption of genomic medicine in clinical practice will impact healthcare costs. This article discusses the potential economic impact of genomic medicine and the challenges that lie ahead.