Real Option Value

Real Option Value in Health Technology Assessment

Real option value (ROV) is the health a treatment adds indirectly, because patients who live longer or stay well longer are still alive and eligible when a better therapy reaches the market. It is counted in the same units as the conventional gain, usually QALYs or life-years, and added to the incremental benefit a cost-effectiveness analysis would otherwise report. Standard models take survival from registration trials run on today's treatment pathway, so they assume nothing better will arrive. In fast-moving areas such as metastatic cancer that assumption can understate a life-extending drug's benefit, and ex ante case studies in four cancers put ROV at about 5 to 18% of conventional value. There is still no consensus on whether it should be counted in health technology assessment, or how.

A novel element of value in the ISPOR taxonomy

The ISPOR Special Task Force on US value frameworks, reporting in 2018, considered twelve potential elements of value. Four (QALYs, net costs, productivity and adherence-improving factors) are conventionally included or considered. Real option value was one of eight more novel elements, alongside reduction in uncertainty, fear of contagion, insurance value, severity of disease, value of hope, equity and scientific spillovers. The task force suggested such elements could enter a net monetary benefit calculation, health state descriptions or a multi-criteria decision analysis.

The name comes from finance, since a treatment that keeps a patient alive buys a right, without an obligation, to use the next therapy. Like the conventional gain, ROV is incremental, since the comparator arm also has survivors who may receive the future therapy.

How a treatment creates or destroys option value

Li and colleagues, in a 2022 guide, identified three primary mechanisms. A current treatment can prolong survival and so raise the share of patients alive when an innovation arrives, or slow progression and so keep more of them eligible for it. It can also directly affect how well the future innovation works.

Li and Garrison, writing in 2024, show that the effect can be negative. A drug that breeds antibiotic resistance can rule out later drugs of the same class, and one antibiotic can create positive ROV through life extension and negative ROV through resistance at once. When a future cure reverses disease, a treatment that improves health now leaves less room for later gain and so also creates negative ROV.

The core calculation

Li and Garrison set out the incremental ROV of a treatment against its control when one innovation is expected at a known time with a known probability. The formula multiplies three quantities, each of which can be estimated separately.

$$\Delta ROV = \left[ S_{trt}(t_f)\, u_{trt}-S_{ctrl}(t_f)\, u_{ctrl} \right] \times p \times \Delta QALY_{future}$$

where $\Delta ROV$ is the real option value in QALYs per patient, $S_{trt}(t_f)$ and $S_{ctrl}(t_f)$ are the proportions alive at time $t_f$ on the current treatment and on the control, $t_f$ is the expected time until the innovation is approved, $u_{trt}$ and $u_{ctrl}$ are the shares of those survivors still eligible for it, $p$ is the probability that it is approved, and $\Delta QALY_{future}$ is the gain an eligible patient receives from the innovation compared with no new technology.

The bracket is the difference between the arms in the proportion of patients who can take up the innovation, and survival at $t_f$ can be read from the trials' Kaplan-Meier curves. Dividing $\Delta ROV$ by the conventional incremental QALYs gives ROV as a share of conventional value. When early reports give only medians, Li and Garrison propose a minimal modelling approach that assumes an exponential distribution, so the median equals the mean multiplied by ln 2. The future gain is then the utility-weighted increase in median time in each health state divided by ln 2.

Forecasting the next innovation

The inputs unique to ROV describe something that has not happened yet. The survival trend approach projects historical mortality improvements from registries such as the US SEER programme. The pipeline approach identifies drugs in phase 2 and 3 trials on ClinicalTrials.gov and estimates their chance of approval, timing and efficacy from published phase transition statistics and early results. With several candidates, Li and Garrison take the probability of at least one approval as one minus the product of the individual probabilities of failure.

Lee and colleagues distinguish ex ante estimates, made at launch with future arrivals treated as uncertain, from ex post estimates, which use the innovations that did arrive, and note that prospective reimbursement calls for the ex ante form.

Worked example with one future therapy

All numbers in this example are illustrative. A new first-line treatment is compared with standard care in a metastatic cancer. The conventional model gives 0.30 incremental QALYs at an incremental cost of GBP 9,000, so the ICER is 9,000 / 0.30 = GBP 30,000 per QALY.

1. Survivors able to use the innovation. One candidate is in phase 2 with a 28.8% chance of approval and another in phase 3 with a 59.0% chance. The chance that both fail is 0.712 × 0.410 = 0.292, so the probability of at least one approval is 0.708, expected at 24 months. At that point 40% of patients on the new treatment and 25% on standard care are alive, and 75% of survivors in each arm are eligible. Eligible survivors are 0.40 × 0.75 = 0.300 and 0.25 × 0.75 = 0.1875, a difference of 0.1125.

2. Gain from the future therapy. Early data suggest the innovation adds 4 months to median time in the progressed state, which has a utility of 0.6. The minimal modelling approximation gives 0.6 × 4 divided by ln 2, which is 3.462 QALY-months, and 3.462 / 12 = 0.2885 QALYs per eligible patient.

3. Undiscounted ROV. The option value is 0.1125 × 0.708 × 0.2885 = 0.0230 QALYs per patient.

4. Discounting. The gain starts two years in. At an illustrative 3.5% annual discount rate the factor is 1 / 1.035² = 0.9335, so the discounted ROV is 0.0230 × 0.9335 = 0.0215 QALYs. Treating the whole gain as starting at month 24 is a simplification.

5. Share of conventional value. The ratio is 0.0215 / 0.30 = 0.072, or 7.2% of the conventional gain.

6. Clinical ROV only. Total QALYs become 0.30 + 0.0215 = 0.3215, and with no future cost the ICER is 9,000 / 0.3215 = GBP 27,994 per QALY.

7. Adding the future therapy's cost. Suppose each patient receiving the innovation incurs GBP 15,000 extra, so its own ICER is 15,000 / 0.2885 = GBP 51,993 per QALY. The expected discounted cost is 0.1125 × 0.708 × 15,000 × 0.9335 = GBP 1,115. Total cost is 9,000 + 1,115 = 10,115, and the ICER is 10,115 / 0.3215 = GBP 31,462 per QALY.

8. Interpretation. Counting the health gain alone lowers the ICER by about GBP 2,000, but adding the future therapy's cost raises it, because the innovation is less cost effective than the current treatment. The direction depends on how the next therapy is priced, which nobody knows at launch.

Effect on the ICER

Li and Garrison extend the incremental cost-effectiveness ratio by adding the expected incremental cost and QALYs of the future innovation to the numerator and the denominator. Step 7 of the example applies this form.

$$ICER_{ROV} = \frac{\Delta C_{trt} + D \times p \times \Delta C_{future}}{\Delta QALY_{trt} + D \times p \times \Delta QALY_{future}}$$

where $ICER_{ROV}$ is the ratio including future innovation, $\Delta C_{trt}$ and $\Delta QALY_{trt}$ are the conventional incremental cost and QALYs, $D$ is the bracketed difference in eligible survivors, $p$ is the probability of approval, and $\Delta C_{future}$ and $\Delta QALY_{future}$ are the incremental cost and QALYs of the future innovation.

An innovation priced at the same ratio as the current treatment leaves the ICER unchanged, a less cost-effective one raises it and a more cost-effective one lowers it. Because future prices are hard to forecast, Li and Garrison suggest threshold analysis of the ICER the innovation would need to change the decision. For ipilimumab the ratio without ROV was USD 229,175 per QALY, and reaching USD 150,000 would have needed a net cost-saving innovation.

Published estimates

For chronic myeloid leukaemia, Sanchez and colleagues put the option value of innovative therapies at 0.76 life-years for a recently diagnosed patient, equivalent to 9% of the average survival gain from existing treatments. Thornton Snider and colleagues found that nivolumab's option value came to 18% of its conventional value in renal cell carcinoma, 5% in squamous and 10% in nonsquamous non-small cell lung cancer. Lee and colleagues estimated that ROV raised the discounted incremental QALYs of first-line alectinib in ALK-positive lung cancer by 11.2%, with a 95% interval from minus 2.14% to 29.29%.

From health records, Lee and colleagues put the ex post ROV of ipilimumab at between 1% and 54% of conventional value for patients diagnosed within two years before cancer immunotherapy became available. A scoping review by Fornaro and colleagues found that estimates of total value including option value lay between plus 7% and plus 469% of conventional value.

Three meanings of option value

Lee and colleagues distinguish three meanings of the term. The one on this page, also called the option value of survival, is the value of living long enough to receive future drugs. Insurance value is the utility of knowing that a service will be available should it be needed, and the third, from financial options theory, is the value of deferring uncertain and irreversible decisions.

Palmer and Smith applied option-pricing techniques to the economic evaluation of health care in 2000, naming uncertainty, the ability to defer a decision and its irreversibility as the determinants of option value. That sense concerns when a decision maker should adopt, which places it close to value of information analysis, whereas the option value of survival concerns what a patient gains by surviving. Li and Garrison also treat the value of hope, the preference of some risk-seeking patients for treatments with a long survival tail, as a separate element that ROV can raise.

Using ROV in value assessment

Li and colleagues recommend a qualitative check of whether the treatment can enable greater benefits from future innovations before any modelling, measurement in the same framework as conventional value, and presentation alongside conventional value as a separate element because of its large uncertainty. Li and Garrison add a 15-item reporting checklist covering the size of ROV, its uncertainty, its interactions with other elements and its effect on the ICER. Lee and colleagues see two possible uses, as a contextual consideration when judging clinical benefit or as a quantitative adjustment to the QALYs.

Weaknesses and frequent misreadings

ROV is a forecast stacked on a model, and most criticism concerns that forecast. Its inputs are rarely observed when a decision is taken, and the cost side is often left out.

1. Treating the estimate as precise. Efficacy of future drugs often comes from response rates in single-arm phase 2 trials, and the alectinib ROV was most sensitive to that efficacy and to uptake.
2. Counting the health gain without the cost. Li and Garrison found only one study that had included the price and cost offsets of the future innovation, and the worked example shows the cost can reverse the change in the ICER.
3. Assuming ROV is always positive. Drug resistance and cures that reverse disease can make it negative.
4. Ignoring the comparator's option value. Survivors on the control arm can also receive the innovation, so only the difference counts.
5. Double counting. Li and Garrison report that the perceived risk of double counting is a common argument for leaving ROV out, since it can interact with adherence, productivity, insurance value and the value of hope.
6. Overlooking who funds the evidence. The alectinib analysis and a real-world melanoma study were both funded by a manufacturer.

Sources

• Fornaro G, Federici C, Rognoni C, Ciani O. Broadening the concept of value: a scoping review on the option value of medical technologies. Value in Health. 2021;24(7):1045-1058. https://doi.org/10.1016/j.jval.2020.12.018
• Lakdawalla DN, Doshi JA, Garrison LP, Phelps CE, Basu A, Danzon PM. Defining elements of value in health care: a health economics approach. An ISPOR Special Task Force Report [3]. Value in Health. 2018;21(2):131-139. https://doi.org/10.1016/j.jval.2017.12.007
• Lee W, Li M, Wong WB, To TM, Garrison LP, Veenstra DL. Modeling the ex post real option value in metastatic melanoma using real-world data. Value in Health. 2021;24(12):1746-1753. https://doi.org/10.1016/j.jval.2021.07.004
• Lee W, Wong WB, Kowal S, Garrison LP, Veenstra DL, Li M. Modeling the ex ante clinical real option value in an innovative therapeutic area: ALK-positive non-small-cell lung cancer. PharmacoEconomics. 2022;40(6):623-631. https://doi.org/10.1007/s40273-022-01147-5
• Li M, Garrison L, Lee W, Kowal S, Wong W, Veenstra D. A pragmatic guide to assessing real option value for medical technologies. Value in Health. 2022;25(11):1878-1884. https://doi.org/10.1016/j.jval.2022.05.014
• Li M, Garrison LP. Incorporating real option value in valuing innovation: a way forward. PharmacoEconomics. 2024;42(Suppl 2):199-210. https://doi.org/10.1007/s40273-024-01352-4
• Palmer S, Smith PC. Incorporating option values into the economic evaluation of health care technologies. Journal of Health Economics. 2000;19(5):755-766. https://doi.org/10.1016/S0167-6296(00)00048-5
• Sanchez Y, Penrod JR, Qiu XL, Romley J, Thornton Snider J, Philipson T. The option value of innovative treatments in the context of chronic myeloid leukemia. American Journal of Managed Care. 2012;18(11 Suppl):S265-S271. https://pubmed.ncbi.nlm.nih.gov/23327458/
• Thornton Snider J, Batt K, Wu Y, Tebeka MG, Seabury S. The option value of innovative treatments for non-small cell lung cancer and renal cell carcinoma. American Journal of Managed Care. 2017;23(10):e340-e346. https://pubmed.ncbi.nlm.nih.gov/29087638/
• Wong WB, To TM, Li M, Lee W, Veenstra DL, Garrison LP. Real-world evidence for option value in metastatic melanoma. Journal of Managed Care and Specialty Pharmacy. 2021;27(11):1546-1555. https://doi.org/10.18553/jmcp.2021.21192