Modeling the Ex Post Real Option Value in Metastatic Melanoma Using Real-World Data

Does Real Option Value Influence Oncologists' Treatment Recommendations? A Survey of US Oncologists

OBJECTIVES

Survival benefit from anticancer treatments, even if modest, improves a patient's chances of accessing future innovations, thereby creating real option value. There is no empirical evidence on the impact of potential future innovations on oncologists' treatment recommendations.

METHODS

We conducted a national online survey of practicing medical and hematological oncologists. We presented a hypothetical metastatic cancer patient with median survival of 6 months under 4 decision-making scenarios with varying expected efficacy and time to arrival of future innovations. We assessed the likelihood of discussing future innovations with their patients and the likelihood that future innovations would influence their current treatment recommendation, as well as factors associated with these 2 outcomes using multivariate logistic regressions.

RESULTS

A total of 201 oncologists completed the survey. When future innovations were expected to improve survival by 6 months and be available in 6 months, 76% of oncologists were likely or very likely to discuss the innovations with their patients, and 68% reported they would influence their current treatment recommendations. A 1-month increase in the expected survival improvement of future innovation was associated with a 1.17 greater odds (95% CI 1.1-1.25) of reporting likely or very likely to discuss future innovations with their patients, whereas a 1-month increase in the expected time to arrival was associated with a 0.91 lower odds (95% CI 0.88-0.94).

CONCLUSIONS

Given that potential future innovations seem to influence oncologists' treatments recommendations, evidence to inform clinical guidelines and value assessments should consider data on real option value impacts to support informed treatment decision making.

Valuing the Societal Impact of Medicines and Other Health Technologies: A User Guide to Current Best Practices

This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories - such as the Second Panel on Cost-Effectiveness's "impact inventory" and International Society of Pharmacoeconomics Outcomes Research (ISPOR) "value flower" - aimed to more comprehensively capture the benefits and costs of new health technologies from a societal perspective. Nevertheless, application of broader value elements in practice has been limited in part because quantifying these elements can be complex, but also because there have been numerous methodological advances since these value inventories have been released (e.g. generalized and risk-adjusted cost effectiveness). To facilitate estimation of treatment value from a societal perspective, this paper provides an updated value inventory - called the GCEA value flower - and a user guide for implementing GCEA for health economics researchers and practitioners. GCEA considers 15 broader value elements across four categories: (i) uncertainty, (ii) dynamics, (iii) beneficiary, and (iv) additional value components. The uncertainty category incorporates patient risk preferences into value assessment. The dynamics category petals account for the evolution of real-world treatment value (e.g. option value) and includes drug pricing trends (e.g. future genericization). The beneficiary category accounts for the fact health technologies can benefit others (e.g. caregivers) and also that society may care to whom health benefits accrue (e.g. equity). Finally, GCEA incorporates additional broader sources of value (e.g. community spillovers, productivity losses). This GCEA user guide aims to facilitate both the estimation of each of these value elements and the incorporation of these values into health technology assessment when conducted from a societal perspective.

The value of the accelerated approval pathway: real-world outcomes associated with reducing the time between innovations

Aim: We investigated the effect of shortening time between innovations with the accelerated approval (AA) pathway on patient outcomes for three solid tumors. Methods: This real-world analysis evaluated patients receiving sequential AA pathway-approved innovations after initial treatment with existing therapies in three solid tumor case studies. Outcomes attributable to AA were estimated and assumed approval occurred at the time of conversion to approval and extrapolated to the US population. Results: Survival gains from accessing innovative therapies were 2.3-3.8-times higher when using the AA pathway. At the US population level, AA was associated with ∼8000 life-years gained across all three tumor case studies. Conclusion: In areas of rapid clinical development, the value of existing therapies can be enhanced by earlier access to AA pathway innovations and should be considered when evaluating the AA program.

A review of HTA guidelines on societal and novel value elements

OBJECTIVES

Health technology assessment (HTA) organizations vary in terms of how they conduct assessments. We assess whether and to what extent HTA bodies have adopted societal and novel elements of value in their economic evaluations.

METHODS

After categorizing "societal" and "novel" elements of value, we reviewed fifty-three HTA guidelines. We collected data on whether each guideline mentioned each societal or novel element of value, and if so, whether the guideline recommended the element's inclusion in the base case, sensitivity analysis, or qualitative discussion in the HTA.

RESULTS

The HTA guidelines mention on average 5.9 of the twenty-one societal and novel value elements we identified (range 0-16), including 2.3 of the ten societal elements and 3.3 of the eleven novel value elements. Only four value elements (productivity, family spillover, equity, and transportation) appear in over half of the HTA guidelines, whereas thirteen value elements are mentioned in fewer than one-sixth of the guidelines, and two elements receive no mention. Most guidelines do not recommend value element inclusion in the base case, sensitivity analysis, or qualitative discussion in the HTA.

CONCLUSIONS

Ideally, more HTA organizations will adopt guidelines for measuring societal and novel value elements, including analytic considerations. Importantly, simply recommending in guidelines that HTA bodies consider novel elements may not lead to their incorporation into assessments or ultimate decision making.

A Pragmatic Guide to Assessing Real Option Value for Medical Technologies

OBJECTIVES

This study aimed to provide recommendations for identifying and implementing real option value (ROV) calculations in value assessment.

METHODS

We identified the primary mechanisms through which ROV can be created based on a theoretical framework for ROV, assessed approaches for predicting future innovations and improvements in health, and described the steps for estimating ROV in a cost-effectiveness analysis framework.

RESULTS

The 3 primary mechanisms by which ROV can be created are when a current treatment (1) prolongs survival to increase the proportion of patients who can receive future innovations, (2) slows disease progression to increase patients' eligibility for future innovations, and (3) directly affects the efficacy of future innovations. We provide 5 recommendations for implementing ROV in value assessment. First, the decision to quantify ROV should be based on a qualitative evaluation of whether the treatment can enable greater benefits from future innovations. Second, ROV should be quantified in the same value assessment framework (eg, cost-effectiveness analysis using quality-adjusted life-year) as the conventional value. Third, method for quantifying ROV should consider data availability, rate of innovation, and sources of future health improvements. Fourth, ROV estimate should be presented alongside the conventional value as a separate element due to its inherently large uncertainty. Finally, generalizability of ROV estimate should be evaluated, and local data should be used when available.

CONCLUSIONS

ROV can arise from a variety of mechanisms that should be considered before investing in an ROV analysis. Calculating ROV includes exploring different approaches for forecasting future innovations and future improvements in health.

Modeling the Ex Ante Clinical Real Option Value in an Innovative Therapeutic Area: ALK-Positive Non-Small-Cell Lung Cancer

OBJECTIVES

A drug that improves survival and/or disease progression can create real option value (ROV)-the additional health gain from future innovations enabled by a longer survival. ROV can be a relevant consideration for both clinical and payer decision-makers. We aimed to estimate the ex ante ROV for first-line (1L) alectinib in anaplastic lymphoma kinase (ALK)-positive non-small-cell lung cancer (NSCLC).

METHODS

We developed a Markov model to estimate life-years (LYs) and quality-adjusted life-years (QALYs) gained with 1L alectinib versus 1L crizotinib due to potential future second-line (2L) drugs. Transition probabilities were derived from the phase 3 trial of 1L alectinib and phase 2 trial of 2L brigatinib. We identified drugs being studied in phase 2 and 3 trials in ALK-positive NSCLC at the time of alectinib's 1L approval and projected the likelihood and timing of their arrival and their potential efficacy based on publicly available data.

RESULTS

The discounted incremental LYs and QALYs for alectinib increased by 12.9% (95% CR - 2.96%, 34.82%; 1.25 vs. 1.11) and 11.2% (95% CR - 2.14%, 29.29%; 1.03 vs. 0.92), respectively, after accounting for ROV. The incremental ROV of alectinib was sensitive to the projected efficacy of future drugs, uptake level, and the hazard ratio of progression-free survival of alectinib (vs. crizotinib).

CONCLUSIONS

Ex ante ROV can be a significant value consideration in therapeutic areas with high levels of expected innovation. The potential efficacy of future drugs and incremental survival with alectinib at the projected time of arrival are important considerations in assessing ROV.

The History and Future of the "ISPOR Value Flower": Addressing Limitations of Conventional Cost-Effectiveness Analysis

OBJECTIVES

Since its publication as part of the 2018 ISPOR Special Task Force (STF) on US Value Assessments, the "ISPOR value flower," with its petals highlighting elements that may be overlooked or underappreciated in conventional drug value assessments, has been discussed and debated. We review the history of the value flower, describe recent developments, and consider implications for future value assessments.

METHODS

We discuss various antecedents to the value flower, as well as conceptual and empirical articles published in the past 4 years.

RESULTS

Since the publication of the ISPOR STF report, researchers have provided more rigorous theoretical and mathematical foundations for certain novel value elements (eg, severity of illness, value of insurance, value of hope) through "generalized risk-adjusted cost-effectiveness analysis," which incorporates risk aversion in people's preferences and uncertainty in treatment outcomes. Empirical estimates are also emerging to support key elements, such as insurance value, real option value, value of hope, and value of knowing. Although health technology assessment bodies have applied or are considering certain elements (eg, severity modifiers to cost-effectiveness thresholds), other elements have yet to gain traction.

CONCLUSIONS

Five years after the STF began its work, the development of novel value measures continues to evolve. Although it is encouraging to see supporting empirical studies emerging, more are needed. Additional efforts are also needed to illustrate how the estimates can be used in the deliberative processes that are integral to health technology assessments.