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

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 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.