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

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.

Incorporating Real Option Value in Valuing Innovation: A Way Forward

BACKGROUND

Considerable progress has been made in defining and measuring the real option value (ROV) of medical technologies. However, questions remain on how to estimate (1) ROV outside of life-extending oncology interventions; (2) the impact of ROV on costs and cost effectiveness; and (3) potential interactions between ROV and other elements of value.

METHODS

We developed a 'minimal modeling' approach for estimating the size of ROV that does not require constructing a full, formal cost-effectiveness model. We proposed a qualitative approach to assessing the level of uncertainty in the ROV estimate. We examined the potential impact of ROV on the incremental cost-effectiveness ratio as well as on the potential interactions between ROV and other elements of value. Lastly, we developed and presented a 15-item checklist for reporting ROV in value assessment.

RESULTS

The minimal modeling approach uses estimates on the efficacy of current treatment and potential future innovation, as well as success rate and length of new treatment development, and can be applied to all types of ROV across disease areas. ROV may interact with the conventional value, value of hope, productivity effects, and insurance value. The impact of ROV on cost effectiveness can be evaluated via threshold analysis.

CONCLUSION

The minimal modeling approach and the checklist developed in this paper simplifies and standardizes the estimation and reporting of ROV in value assessment. Systematically including and reporting ROV in value assessment will minimize bias and improve transparency, which will help improve the credibility of ROV research and acceptance by stakeholders.

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.