After 20 Years of Using Economic Evaluation, Should NICE be Considered a Methods Innovator?

Commissioning [Integrated] Care in England: An Analysis of the Current Decision Context

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Patient preferences for breast cancer treatments: a discrete choice experiment in France, Ireland, Poland, Spain

Aim: To understand breast cancer patients' trade-offs when choosing treatments and to identify the most important treatment attributes which drive decisions. Materials & methods: A discrete choice experiment was conducted in France, Ireland, Poland and Spain. Progression-free survival, febrile neutropenia, pain, functional well-being and out-of-pocket payment were the treatment attributes. Results: 371 patients were willing to pay €6896 per year for 1 additional year of progression-free survival, €17,288 per year for perfect functional well-being and €15,138 for one pain-free year. Patients are willing to trade off progression-free survival months for better functional abilities and less pain. Conclusion: Patient preferences should be considered by regulatory agencies, reimbursement bodies, payors and clinicians for best treatment choices for the individuals.

Broadening the Concept of Value: A Scoping Review on the Option Value of Medical Technologies

OBJECTIVES

A recent debate in health economics and outcomes research community identified option value as one of the elements warranting consideration in the assessment of medical technologies. To conduct a scoping review of contributions on option value in the healthcare sector and identify relevant conceptual aspects and methods used to incorporate it in standard economic evaluations.

METHODS

A systematic search was conducted up to July 2020 to identify contributions from electronic bibliographic database and gray literature. Data on the proposed definitions of option value, theoretical implications of its use in economic evaluations, and methods used to estimate it were extracted and analyzed.

RESULTS

We found 57 eligible studies. Three different definitions emerged: insurance value, real option value, and option value of survival. Focusing on the latter (24 studies), we analyzed in depth 8 empirical applications across 7 therapeutic areas. The most relevant methodological challenges were on the perspective used in economic evaluations and how to robustly manage forecasting uncertainty, update cost-effectiveness thresholds, and avoid double-counting issues. For empirical studies assessing the total value of the technology, including option value, estimates ranged from +7% to +469% of its conventional value.

CONCLUSIONS

This review synthesizes theoretical and empirical aspects on option value of healthcare technologies and proposes a terminology to distinguish 3 different concepts identified. Future work should focus primarily on agreeing on whether option value should be included in economic evaluations and, if so, on developing and validating reliable methods for its ex-ante estimation.

Can the Consideration of Societal Costs Change the Recommendation of Economic Evaluations in the Field of Rare Diseases? An Empirical Analysis

OBJECTIVES

To analyze whether the adoption of a societal perspective would alter the results and conclusions of economic evaluations for rare disease-related healthcare technologies.

METHODS

A search strategy involving all the active substances considered as orphan drugs by the European Medicines Agency plus a list of 76 rare diseases combined with economic-related terms was conducted on Medline and the Cost-Effectiveness Registry from the beginning of 2000 until November 2018. We included studies that considered quality-adjusted life years as an outcome, were published in a scientific journal, were written in English, included informal care costs or productivity losses, and separated the results according to the applied perspective.

RESULTS

We found 14 articles that fulfilled the inclusion criteria. Productivity losses were considered in 12 studies, the human capital approach being the method most frequently used. Exclusively, informal care was considered in 2 articles, being valued through the opportunity cost method. The 14 articles selected resulted in 26 economic evaluation estimations, from which incremental cost-utility ratio values changed from cost-effective to dominant in 3 estimates, but the consideration of societal costs only modified the authors' conclusion in 1 study.

CONCLUSIONS

The presence of societal costs in the economic evaluation of rare diseases did not affect the conclusions of the studies except in a single specific case. In those studies where the societal perspective was considered, we did not find significant changes in the economic evaluation results due to the higher costs of treatments and the low quality-adjusted life-years gained.

What Did Time Tell Us? A Comparison and Retrospective Validation of Different Survival Extrapolation Methods for Immuno-Oncologic Therapy in Advanced or Metastatic Renal Cell Carcinoma

BACKGROUND

The immuno-oncologic (IO) mechanism of action may lead to an overall survival (OS) hazard that changes over time, producing shapes that standard parametric extrapolation methods may struggle to reflect. Furthermore, selection of the most appropriate extrapolation method for health technology assessment is often based on trial data with limited follow-up.

OBJECTIVE

To examine this problem, we fitted a range of extrapolation methods to patient-level survival data from CheckMate 025 (NCT01668784, CM-025), a phase III trial comparing nivolumab with everolimus for previously treated advanced renal cell carcinoma (aRCC), to assess their predictive accuracy over time.

METHODS

Six extrapolation methods were examined: standard parametric models, natural cubic splines, piecewise models combining Kaplan-Meier data with an exponential or non-exponential distribution, response-based landmark models, and parametric mixture models. We produced three database locks (DBLs) at minimum follow-ups of 15, 27, and 39 months to align with previously published CM-025 data. A three-step evaluation process was adopted: (1) selection of the distribution family for each method in each of the three DBLs, (2) internal validation comparing extrapolation-based landmark and mean survival with the latest CM-025 dataset (minimum follow-up, 64 months), and (3) external validation of survival projections using clinical expert opinion and long-term follow-up data from other nivolumab studies in aRCC (CheckMate 003 and CheckMate 010).

RESULTS

All extrapolation methods, with the exception of mixture models, underestimated landmark and mean OS for nivolumab compared with CM-025 long-term follow-up data. OS estimates for everolimus tended to be more accurate, with four of the six methods providing landmark OS estimates within the 95% confidence interval of observed OS as per the latest dataset. The predictive accuracy of survival extrapolation methods fitted to nivolumab also showed greater variation than for everolimus. The proportional hazards assumption held for all DBLs, and a dependent log-logistic model provided reliable estimates of longer-term survival for both nivolumab and everolimus across the DBLs. Although mixture models and response-based landmark models provided reasonable estimates of OS based on the 39-month DBL, this was not the case for the two earlier DBLs. The piecewise exponential models consistently underestimated OS for both nivolumab and everolimus at clinically meaningful pre-specified landmark time points.

CONCLUSIONS

This aRCC case study identified marked differences in the predictive accuracy of survival extrapolation methods for nivolumab but less so for everolimus. The dependent log-logistic model did not suffer from overfitting to early DBLs to the same extent as more complex methods. Methods that provide more degrees of freedom may accurately represent survival for IO therapy, particularly if data are more mature or external data are available to inform the long-term extrapolations.

The political economics of cancer drug discovery and pricing

Drug discoveries can, when used appropriately, save lives. Since 1970, cancer death rates among people aged under 65 have halved in countries such as the USA and the UK. Despite pharmaceutical market imperfections and fears about the prices of new treatments, further progress should be possible during the 2020s. Anticancer medicine outlays account for 0.1-0.2% of the gross domestic product (GDP) of developed countries. Total cancer service spending typically stands at ∼0.8% of GDP. The affordability of these sums is a political calculation. Improvements in the efficiency of drug development and global access to effective therapies are desirable. However, from a public interest perspective, these goals should not be pursued in ways that understate the value of better treatment outcomes and threaten the funding available for ongoing innovation.

Clinical Adoption of Advanced Therapies: Challenges and Opportunities

As the cell and gene therapy field matures the powerful therapeutic potential of these innovative therapies is starting to be shown, particularly in the fields of oncology and childhood immune deficiency diseases. However, as more therapies enter late stage clinical trials, advances and innovation are required in manufacturing, logistics, regulation, reimbursement and the healthcare setting to ensure that systems are in place to support wider clinical adoption of these promising treatments. A window of opportunity exists to implement new methodologies for best practice in both the ability to manufacture products reproducibly at scale, as well as ensuring healthcare systems are not overwhelmed by the variety and complexity of these new therapies and the additional burden they will place on already stretched facilities. If all interested parties work together it will be possible for the sector to develop the necessary processes, skilled staff and infrastructure needed as more treatments move from clinical trial to marketed products.

Conducting Value for Money Analyses for Non-randomised Interventional Studies Including Service Evaluations: An Educational Review with Recommendations

This article provides an educational review covering the consideration of conducting ‘value for money’ analyses as part of non-randomised study designs including service evaluations. These evaluations represent a vehicle for producing evidence such as value for money of a care intervention or service delivery model. Decision makers including charities and local and national governing bodies often rely on evidence from non-randomised data and service evaluations to inform their resource allocation decision-making. However, as randomised data obtained from randomised controlled trials are considered the ‘gold standard’ for assessing causation, the use of this alternative vehicle for producing an evidence base requires careful consideration. We refer to value for money analyses, but reflect on methods associated with economic evaluations as a form of analysis used to inform resource allocation decision-making alongside a finite budget. Not all forms of value for money analysis are considered a full economic evaluation with implications for the information provided to decision makers. The type of value for money analysis to be conducted requires considerations such as the outcome(s) of interest, study design, statistical methods to control for confounding and bias, and how to quantify and describe uncertainty and opportunity costs to decision makers in any resulting value for money estimates. Service evaluations as vehicles for producing evidence present different challenges to analysts than what is commonly associated with research, randomised controlled trials and health technology appraisals, requiring specific study design and analytic considerations. This educational review describes and discusses these considerations, as overlooking them could affect the information provided to decision makers who may make an ‘ill-informed’ decision based on ‘poor’ or ‘inaccurate’ information with long-term implications. We make direct comparisons between randomised controlled trials relative to non-randomised data as vehicles for assessing causation; given ‘gold standard’ randomised controlled trials have limitations. Although we use UK-based decision makers as examples, we reflect on the needs of decision makers internationally for evidence-based decision-making specific to resource allocation. We make recommendations based on the experiences of the authors in the UK, reflecting on the wide variety of methods available, used as documented in the empirical literature. These methods may not have been fully considered relevant to non-randomised study designs and/or service evaluations, but could improve and aid the analysis conducted to inform the relevant value for money decision problem.