Option value is the benefit a therapy provides patients by enabling them to survive to the next innovation.
Objectives: To develop a model of the option value a therapy provides by enabling patients to live to see subsequent innovations and to apply the model to the case of nivolumab in renal cell carcinoma (RCC) and non—small cell lung cancer (NSCLC).
Study Design: A model of the option value of nivolumab in RCC and NSCLC was developed and estimated.
Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry and published clinical trial results were used to estimate survival curves for metastatic cancer patients with RCC, squamous NSCLC, or nonsquamous NSCLC. To estimate the conventional value of nivolumab, survival with the pre-nivolumab standard of care was compared with survival with nivolumab assuming no future innovation. To estimate the option value of nivolumab, long-term survival trends in RCC and squamous and nonsquamous NSCLC were measured in SEER to forecast mortality improvements that nivolumab patients may live to see.
Results: Compared with the previous standard of care, nivolumab extended life expectancy by 6.3 months in RCC, 7.5 months in squamous NSCLC, and 4.5 months in nonsquamous NSCLC, according to conventional methods. Accounting for expected future mortality trends, nivolumab patients are likely to gain an additional 1.2 months in RCC, 0.4 months in squamous NSCLC, and 0.5 months in nonsquamous NSCLC. These option values correspond to 18%, 5%, and 10% of the conventional value of nivolumab, respectively.
Conclusions: Option value is important when valuing therapies like nivolumab that extend life in a rapidly evolving area of care.
Am J Manag Care. 2017;23(10):e340-e346Takeaway Points
Cancer is a leading cause of death in the United States,1 with rising prevalence. In 1995, there were 7.1 million individuals in the US who had ever been diagnosed with cancer (2.7% of the population)2; by 2012, there were 13.4 million such individuals (4.3%).3 Although cancer mortality rates are declining,4 prevalence is expected to increase because of population aging,5,6 advances in cardiovascular care, earlier cancer detection, and life-extending treatment.7,8
Increasing cancer prevalence translates to greater demand for oncology care, with implications for payers. Given growing pressures on payers’ budgets and debate over the cost of cancer treatment,9,10 value is an increasingly important consideration in coverage and access decisions. Moreover, the pressures on payers will increase as cancer prevalence rises, further reinforcing the importance of value.5
As value has grown in importance in assessing cancer care, organizations have offered recommendations on measuring value. For example, the National Comprehensive Cancer Network and the American Society of Clinical Oncology recently published frameworks for considering a therapy’s efficacy and cost to determine its value.11,12 The Institute of Clinical and Economic Review, an independent health technology assessment (HTA) organization, uses cost-effectiveness analysis (CEA) and budget impact analysis to measure therapies’ value,13 an approach common in Europe.14,15
Traditional methods of CEA measure the value of an innovation by comparing benefits such as survival gains and improved quality of life with costs, assuming no other health technology improvements besides the one in question.16 However, such methods ignore aspects of therapies that matter to patients, like the value of hope, insurance value to healthy individuals, and the increased value people place on life near its end.17-20
Such components of value can be difficult to quantify in HTA because they are subjective valuations based on unobserved patient preferences. However, 1 relevant component of value is rooted in real-world survival data, the “option value” of therapy. Option value is the benefit a therapy provides patients by enabling them to survive to the next innovation. For example, in cancer care, any survival gains today hold additional value because they increase the likelihood that a patient may live to access even more effective treatment in the future. This concept is particularly important in disease areas with traditionally poor outcomes and rapid innovation.
Although metastatic non—small cell lung cancer (NSCLC) and metastatic renal cell carcinoma (RCC) have had low survival rates,21,22 innovation to treat both cancers is currently rapid. A number of breakthrough targeted therapies and immuno-oncology (IO) therapies, which harness the immune system to fight cancer, are available, such as nivolumab, pembrolizumab, osimertinib, and ceritinib for NSCLC and nivolumab and lenvatinib for RCC.23,24 These therapies are improving 1 or more clinical endpoints, such as survival rates for cancers with traditionally poor survival odds.23,24 Furthermore, the pipelines for both tumors are active.25,26
Given the proliferation of treatment options and the prospect of future innovation, payers, policymakers, clinicians, and patients require multiple criteria to make care-related decisions.27 A therapy’s option value accounts for the future benefits of a life-extending therapy in addition to its immediate survival effects. With that in mind, we looked at the option value of nivolumab, the first IO therapy given breakthrough status and approved by the FDA for NSCLC and RCC.23,24 NSCLC is histologically divided into squamous and nonsquamous (adenocarcinoma, large cell) tumor types,21 and individuals face different prognosis and treatment options depending on the histology. Squamous NSCLC (about 25%-30% of all lung cancers) is typically smoking-related, whereas nonsquamous NSCLC is the most common type of lung cancer in nonsmokers (although smokers are still at greater risk than nonsmokers).28 We examined squamous and nonsquamous NSCLC separately.
We adapted a framework for estimating a therapy’s option value19,20,29,30 to the contexts of metastatic NSCLC and metastatic RCC. In particular, we examined the option value of nivolumab, the first programmed cell death protein-1 (PD-1) inhibitor in both disease states. In order to estimate nivolumab’s option value, a survival curve for patients taking the pre-nivolumab standard of care was needed first. The pre-nivolumab survival curves were modified using mortality hazard ratios (HRs) from clinical trial publications to obtain a nivolumab survival curve in each setting. Forecasting methods were used to project likely future survival improvements patients with either NSCLC or RCC may live to see. The conventional survival gain from nivolumab was obtained by comparing life expectancy with nivolumab, assuming no future innovation, against life expectancy with the pre-nivolumab standard of care. The option value of nivolumab was obtained by comparing life expectancy with nivolumab under 2 scenarios: allowing for likely future survival gains versus assuming no future innovation. Finally, the option value estimates were converted to economic terms. Additional methodological detail is available in the eAppendix (eAppendices available at ajmc.com).
Survival trends in metastatic NSCLC and metastatic RCC were estimated using the Surveillance, Epidemiology and End Results (SEER) cancer registry.31 SEER, which tracks cancer incidence and mortality using data reported by registries across the United States, currently covers about 30% of the US population.32
Long-term all-cause mortality rates were taken from the Human Mortality Database (HMD).33 The HMD contains detailed population and mortality data for 37 countries, and currently provides US life tables for 1933 through 2013.
Survival curves from SEER were modified to reflect nivolumab patients’ survival using mortality HRs from clinical trial publications.34-36 Specifically, in both squamous and nonsquamous NSCLC, nivolumab was compared with docetaxel, and in RCC, nivolumab was compared with everolimus. These comparisons reflect those in the trials noted on the nivolumab FDA label.37 The comparator drugs were assumed to represent the pre-nivolumab standard of care.
We identified 3 study populations: metastatic squamous NSCLC, metastatic nonsquamous NSCLC, and metastatic RCC. First, we identified patients with NSCLC in SEER by requiring a primary cancer site of “lung or bronchus” or “trachea.” We distinguished squamous from nonsquamous histology types using International Classification of Diseases for Oncology, Third Edition (ICD-O-3) histology codes. For both histology types, we defined metastatic NSCLC as American Joint Committee on Cancer (AJCC) stage IV, IIIb, or III with no surgery. The latter category addressed the fact that not all patients have a substage listed (eg, IIIa vs IIIb), and a lack of surgery suggested the case was inoperable.
To identify the RCC population, we required a primary site of “kidney,” “renal pelvis,” or “ureter,” and identified the RCC histology using ICD-O-3 histology codes. To limit to patients with RCC with metastatic disease, we selected the “distant” stage using “SEER historic stage A.” Although AJCC staging is commonly used in clinical practice for treatment decisions and in clinical trials for eligibility,38 it was not well documented for RCC in SEER before 2004. Because metastatic RCC is a smaller sample than NSCLC, preserving sample size was important. Moreover, in order to track longer-term trends, it was necessary to be able to identify patients before 2004. In general, SEER distant stage approximates AJCC stage IV. Therefore, we identified metastatic RCC using the SEER distant stage.
We estimated the option value of nivolumab in the 3 populations by taking these 5 steps.
Step 1: estimate pre-nivolumab survival curves. To establish a pre-nivolumab baseline, we estimated survival curves using the SEER data. We identified metastatic squamous and nonsquamous NSCLC and metastatic RCC patients diagnosed between 2001 and 2010. Survival curves for each population were estimated parametrically using a log-normal distribution. Covariates included age at diagnosis, age squared, gender, race, ethnicity, marital status, tumor grade, and a quadratic time trend.
Our survival estimation was limited by the patients’ duration of follow-up in SEER. Although we preferred using patients with a more recent diagnosis due to the pace of innovation in cancer care, this limited the number of years of follow-up available. Rather than project survival for many years beyond the duration of follow-up in the SEER data, we assumed that the minority of patients surviving more than 20 years past diagnosis had the same mortality rates as the general population, using US life tables from the HMD.
Specifically, we calculated mortality rates in the first 20 years after diagnosis using the estimated survival curves from SEER and then appended the HMD mortality rates. This lifetime mortality table was used as an input in the Lee-Carter model described in Step 2.
Step 2: forecast survival improvements. The SEER survival curves estimated in Step 1 showed a clear trend of improving survival over time in all 3 populations. Based on these trends, we used the Lee-Carter method39,40 to forecast survival improvements for each population. The Lee-Carter method involves modeling age-specific mortality rates as a function of a long-term trend in overall mortality improvements and an age-specific response to the overall trend. This method is widely used as a benchmark for long-term mortality forecasts, including by the US Census Bureau and the Social Security Administration.40
Step 3: estimate survival with nivolumab. To obtain survival curves for patients taking nivolumab in each population, we began with the pre-nivolumab survival curves estimated in Step 1. Based on the FDA label, which calls for second- or later-line use in both the NSCLC and RCC indications,37 we assumed that nivolumab would be given as a second-line therapy starting 1 year after diagnosis. Following the clinical trial evidence,34-36 we assumed that nivolumab patients would experience diminished mortality risk compared with patients on the prior standard of care. Specifically, we applied the mortality HRs from nivolumab trial publications to the pre-nivolumab survival curves estimated in Step 1. We conservatively assumed that any mortality benefits of nivolumab would disappear 4 years after beginning nivolumab therapy.
Step 4: calculate option value in terms of life expectancy gains. Using the survival curves from Steps 1 and 3 and the survival forecasts from Step 2, we calculated the undiscounted life expectancies of patients in each population for several scenarios. First, we calculated life expectancy under the pre-nivolumab standard of care, assuming no future innovation; call this LEpre-nivo. Second, we calculated life expectancy with nivolumab, assuming no future innovation; call this LEnivo. Finally, we calculated life expectancy with nivolumab allowing for future innovation based on the forecasts from Step 2; call this ELnivo.
We calculated the conventional survival gain with nivolumab by comparing life expectancy with nivolumab to that with the pre-nivolumab standard of care. Specifically:
Conventional survival gain with nivolumab = LEnivo — LEpre-nivo
To calculate the option value of nivolumab, we compared life expectancy with nivolumab assuming no further innovation to expected lifetime with nivolumab allowing for future innovation. Specifically:
Option value of nivolumab = ELnivo — LEnivo
Step 5: calculate option value in economic terms. Finally, we expressed the conventional and option values in economic terms by applying an economic value of a life year to the additional life years gained. We selected a mid-range value from the literature of $150,000 per life year41,42 and applied a discount rate of 3% to obtain present discounted values. The per-patient option value was multiplied by the estimated size of each patient population to obtain the total economic values in each population. Population sizes were estimated using several sources.28,43-47
To test the robustness of the study results to the underlying assumptions, several sensitivity analyses were performed. First, we estimated an alternative parametric survival model (log logistic). This tested the sensitivity of the results to our choice of parametric model. Second, we estimated survival exclusively using the parametric model rather than taking HMD mortality rates after 20 years. This tested the effect of our use of the HMD data. Third, we assumed nivolumab would be taken in the first year after diagnosis, rather than the second. This reflected the fact that metastatic cancer can progress rapidly and, therefore, some patients could potentially reach second-line therapy in the first rather than second year from diagnosis. Fourth, we varied the duration post diagnosis that nivolumab would be effective, from 2 to 10 years. This addressed the limited duration of clinical trial data and the accompanying uncertainty about the duration of nivolumab’s effects. Fifth, we estimated nivolumab’s mortality benefits using the confidence intervals of the published mortality HRs in order to address uncertainty about the exact magnitude of nivolumab’s effects of survival. Sixth, we varied the discount rate from 0% to 6%. Seventh, we varied the value of a life year from $50,000 to $250,000.26,27 Last, we varied the stage definition used for the selection of the SEER RCC data, using AJCC stage IV instead of the historic “distant” stage. (We used the historic stage definition in our base case analysis because it was better populated and we wanted to test the sensitivity of our results to this choice.)
The Table presents descriptive statistics on the 3 study populations in SEER. The nonsquamous NSCLC population was the largest, at 210,419 individuals, followed by squamous NSCLC (49,194) and RCC (12,868). The RCC population was younger than the squamous and nonsquamous NSCLC populations, with average ages of 64.5, 69.0, and 68.0 years, respectively. A higher share of patients with nonsquamous NSCLC (44%) was female compared with squamous NSCLC (35%) and RCC (33%). Patients with RCC were more likely to be married (60%) compared with patients with squamous (52%) or nonsquamous (53%) NSCLC. Racial composition was similar, with whites composing over 80% of all 3 groups. Median survival was 8 months in RCC and 7 months in both squamous and nonsquamous NSCLC.
All 3 populations showed an increasing 1-year survival rate in the 2001 to 2010 SEER data (Figure 1). Projected survival in 2011 to 2060 according to the Lee-Carter method is shown via the dashed lines. Survival improved the most quickly in RCC (0.44% per year), followed by nonsquamous NSCLC (0.39%) and squamous NSCLC (0.27%).
Life Expectancy Estimates and Option Value
Life expectancy for patients taking the pre-nivolumab standard of care was estimated to be 18.2 months in RCC, 11.7 months in nonsquamous NSCLC, and 11.6 months in squamous NSCLC. Ignoring future innovations, patients taking nivolumab were expected to survive 24.5 months with RCC, 16.2 months with nonsquamous NSCLC, and 19.1 months with squamous NSCLC, implying gains of 6.3 months, 4.5 months, and 7.5 months, respectively. These gains are shown in blue in Figure 2 and represent the conventional survival gains from nivolumab.
However, during the time that nivolumab patients’ survival was extended, we allowed for additional innovations to come to market. Incorporating expected innovation, nivolumab extends survival by an additional 1.2 months in RCC, 0.5 months in nonsquamous NSCLC, and 0.4 months in squamous NSCLC. These additional gains due to option value represent 18% of nivolumab’s conventionally estimated survival gain in RCC, 10% in nonsquamous NSCLC, and 5% in squamous NSCLC.
Valuing the per-patient survival gains over the full incident metastatic population with each tumor, we found a conventional economic value from nivolumab of $775 million in RCC and an option value of $105 million (Figure 3). Due to its higher incidence, economic values were larger in NSCLC. The conventional value of nivolumab was $2.6 billion in nonsquamous NSCLC and $2.0 billion in squamous NSCLC, while the option values were $203 million and $73 million, respectively. Although these valuations are based on a mid-range value of a quality-adjusted life year, it should be noted that debate exists about the best way to provide economic valuations of incremental changes in survival, such as those in this study.41,42,48
The study results were robust to a variety of specifications. Across the 3 populations, the value of a life year was particularly influential on option value in economic terms. Varying the value from $50,000 to $250,000 changed the option value from $35 to $174 million in RCC, $68 to $339 million in nonsquamous NSCLC, and $24 to $122 million in squamous NSCLC. Varying the discount rate was also influential, with a 0% discount rate leading to the largest option values ($180 million in RCC, $316 million in nonsquamous NSCLC, $118 million in squamous NSCLC) and 6% to the lowest ($67 million in RCC, $141 million in nonsquamous NSCLC, $49 million in squamous NSCLC). The nivolumab mortality HR was particularly influential on option value in life expectancy terms, with values at the lower (higher) end of the confidence intervals producing the highest (lowest) option value (1.42 vs 0.88 months in RCC, 0.56 vs 0.35 in nonsquamous NSCLC, 0.46 vs 0.27 in squamous NSCLC). Assuming a longer duration of benefit from nivolumab and allowing nivolumab therapy to start in the first, rather than second, year after diagnosis also led to greater option value. Results were qualitatively similar regardless of the parametric model used, whether HMD was used, and the stage definition. (Additional detail is available in the eAppendix.)
The study results show that incorporating option value can substantially increase the conventionally calculated value of a therapy. The option value of nivolumab accounts for an additional gain of 1.2 months (18% of conventional survival gain) in metastatic RCC, 0.5 months (10%) in nonsquamous NSCLC, and 0.4 months (5%) in squamous NSCLC. Over the full US incident population, this amounted to an option value of $105 million in metastatic RCC. Given the high incidence of lung cancer, the option value was greater in nonsquamous NSCLC ($203 million) and squamous NSCLC ($73 million). These results show that option value is quantitatively meaningful.
This concept of option value has been introduced in other diseases, such as HIV/AIDS, breast cancer, and chronic myeloid leukemia,20,29,30 where innovation alters survival and therefore the value of life near its end.19 This study adds to the existent literature, showing that option value can be important in various healthcare contexts with rapid innovation, as seen in metastatic RCC and NSCLC.
Although our analysis focused on metastatic RCC and NSCLC, the methodology can be applied in other therapeutic areas. The study results demonstrate the need for payers and providers to be aware of attributes of therapies that patients value that are not reflected in traditional value metrics, like the value of hope, the value of life near its end, and the insurance value of therapy.17-19 Option value is closely related to these concepts, since the option of surviving to see new therapies may give patients hope. Such considerations may be particularly important for patients near the end of life, for example, with a terminal cancer diagnosis. Moreover, option value is especially relevant to patients with metastatic cancer because of rapid innovation in this area.
This study's results suggest promising directions for future research of relevance to payers, providers, and patients. Option value studies can better inform payers and providers of expected survival gains in areas of rapid innovation, as well as their economic value. Further research is needed to understand the patient perspective on innovative therapies and their value. For example, patients could be surveyed on their perceptions and valuations of option value in different disease states. Such research could help to improve the care of patients being treated for serious illnesses, such as cancer.
This study has limitations. First, SEER is an incidence sample; therefore, we assumed second-line therapy started 1 year after diagnosis. Second, the SEER population could not be made perfectly comparable with the trial population, thereby affecting life expectancy estimates. In particular, because SEER is an incidence sample and does not report therapies used over time, it was not possible to select only those patients who took the second-line therapies with which nivolumab was compared in the trials (docetaxel in NSCLC and everolimus in RCC). Instead, we obtained the nivolumab survival curves by applying the mortality HRs from the trials to the pre-nivolumab survival curves from SEER. Therefore, both the pre-nivolumab and nivolumab survival estimates in our model are based on the SEER population and should be similarly affected by this assumption. As a result, the effect on our option value estimates should be minimal.
Third, the option value calculations are based on forecasted mortality improvements, which are inherently less accurate than historical data. However, this approach allows us to calculate option value for current treatments rather than focusing on purely historical examples. To do so, we used the Lee-Carter method, which is widely used to forecast improvements in mortality, including by the US Census Bureau and Social Security Administration.40
In addition, sensitivity analyses showed that option value was quantitatively important across a range of modeling assumptions. In particular, the results were robust to alternative specifications of the survival model, assumptions on the use of nivolumab and its effects, and alternative stage definitions.
Recent innovations in oncology have allowed patients to live long enough to gain access to more effective future treatments; this is their option value. This study quantifies the option value of nivolumab for metastatic RCC and metastatic NSCLC and shows that it is substantial. Option value is therefore important to patients, payers, providers, and society as a whole.Author Affiliations: Precision Health Economics (JTS, KB, YW, MGT, SS), Los Angeles, CA; Wake Forest University School of Medicine (KB), Winston-Salem, NC; University of Southern California Schaeffer Center (SS), Los Angeles, CA.
Source of Funding: Financial support for this research was provided by Bristol-Myers Squibb.
Author Disclosures: Dr Snider is an employee of and holds equity in Precision Health Economics, which receives consulting payments from life sciences companies and received consulting fees from Bristol-Myers Squibb for the conduct of this study. Dr. Wu and Ms. Tebeka were employees of Precision Health Economics at the time this study was conducted. Drs Batt and Seabury are consultants for Precision Health Economics.
Authorship Information: Concept and design (JTS, KB, YW, SS); acquisition of data (YW); analysis and interpretation of data (JTS, KB, YW, MGT, SS); drafting of the manuscript (JTS, KB, MGT, SS); critical revision of the manuscript for important intellectual content (JTS, KB, YW, MGT, SS); statistical analysis (JTS, YW, SS); provision of patients or study materials (YW, MGT); obtaining funding (JTS); administrative, technical, or logistic support (YW, MGT); and supervision (JTS, SS).
Address Correspondence to: Seth Seabury, PhD, University of Southern California, USC Schaeffer Center, 635 Downey Way, Los Angeles, CA 90089-3333. E-mail: firstname.lastname@example.org.REFERENCES
1. Leading causes of death. CDC website. http://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm. Updated March 17, 2017. Accessed September 14, 2017.
2. Polednak AP. Estimating the prevalence of cancer in the United States. Cancer. 1997;80(1):136-141.
3. SEER cancer statistics review, 1975-2012. Surveillance Epidemiology and End Results Program website. https://seer.cancer.gov/archive/csr/1975_2012/. Published April 2015. Updated November 18, 2015. Accessed September 14, 2017.
4. Simon S. Cancer statistics report: death rate down 23% in 21 years. American Cancer Society website. http://www.cancer.org/cancer/news/news/cancer-statistics-report-death-rate-down-23-percent-in-21-years. Published January 7, 2016. Accessed May 17, 2016.
5. Smith BD, Smith GL, Hurria A, Hortobagyi GN, Buchholz TA. Future of cancer incidence in the United States: burdens upon an aging, changing nation. J Clin Oncol. 2009;27(17):2758-2765. doi: 10.1200/JCO.2008.20.8983.
6. Population projections of the United States by age, sex, race, and Hispanic origin: 1995 to 2050. US Census Bureau website. https://www.census.gov/prod/1/pop/p25-1130/p251130.pdf. Published February 1996. Accessed September 14, 2017.
7. Honore B, Lleras-Muney A. Bounds in competing risks models and the war on cancer. Econometrica. 2006;74(6). doi: 10.1111/j.1468-0262.2006.00722.x.
8. Lakdawalla DN, Sun EC, Jena AB, Reyes CM, Goldman DP, Philipson TJ. An economic evaluation of the war on cancer. Journal of Health Economics. 2010;29(3). doi: 10.1016/j.jhealeco.2010.02.006.
9. Marsa L. The high cost of cancer care: your money or your life? Newsweek website. http://www.newsweek.com/2015/07/31/high-cost-cancer-care-your-money-or-your-life-356369.html. Published July 23, 2015. Accessed May 17, 2016.
10. Siddiqui M, Rajkumar SV. The high cost of cancer drugs and what we can do about it. Mayo Clin Proc. 2012;87(10):935-943. doi: 10.1016/j.mayocp.2012.07.007.
11. Schnipper LE, Davidson NE, Wollins DS, et al; American Society of Clinical Oncology. American Society of Clinical Oncology statement: a conceptual framework to assess the value of cancer treatment options. J Clin Oncol. 2015;33(23):2563-2577. doi: 10.1200/JCO.2015.61.6706.
12. NCCN Framework for Resource Stratification of NCCN Guidelines (NCCN Framework). National Comprehensive Cancer Network website. https://www.nccn.org/framework/. Published 2017. Accessed September 14, 2017.
13. California Technology Assessment Forum. Institute for Clinical and Economic Review website. http://icer-review.org/programs/ctaf. Published 2017. Accessed September 14, 2017.
14. The German Agency for Health Technology Assessment. German Institute of Medical Documentation and Information website. https://www.dimdi.de/static/en/hta/. Published 2017. Accessed September 14, 2017.
15. National Institute for Health and Care Excellence website. https://www.nice.org.uk/. Published 2017. Accessed September 14, 2017.
16. Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med. 1977;296(13):716-721. doi: 10.1056/NEJM197703312961304.
17. Lakdawalla D, Malani A, Reif J. The insurance value of medical innovation. The National Bureau of Economic Research website. http://www.nber.org/papers/w21015. Published March 2015. Accessed September 14, 2017.
18. Lakdawalla DN, Romley JA, Sanchez Y, Maclean JR, Penrod JR, Philipson T. How cancer patients value hope and the implications for cost-effectiveness assessments of high-cost cancer therapies. Health Aff (Millwood). 2012;31(4):676-682. doi: 10.1377/hlthaff.2011.1300.
19. Philipson TJ, Becker G, Goldman D, Murphy KM. Terminal care and the value of life near its end. The National Bureau of Economic Research website. http://www.nber.org/papers/w15649. Published January 2010. Accessed September 14, 2017..
20. Philipson TJ, Jena AB. Who benefits from new medical technologies? estimates of consumer and producer surpluses for HIV/AIDS drugs. Forum Health Econ Policy. 2006;9(2). doi: 10.2202/1558-9544.1005.
21. Non-small cell lung cancer treatment (PDQ)—health professional version. National Cancer Institute website. http://www.cancer.gov/types/lung/hp/non-small-cell-lung-treatment-pdq. Updated March 31, 2017. Accessed September 14, 2017.
22. Survival rates for kidney cancer by stage. American Cancer Society website. https://www.cancer.org/cancer/kidney-cancer/detection-diagnosis-staging/survival-rates.html. Published February 24, 2014. Updated May 16, 2016. Accessed May 17, 2016.
23. Breakthrough therapies. Friends of Cancer Research website. http://www.focr.org/breakthrough-therapies?title=&field_sponsor_value=&field_therapy_category_tid%5B%5D=40&field_fda_status_value%5B%5D=0&field_fda_status_value%5B%5D=1. Updated August 1, 2017. Accessed September 14, 2017.
24. Fact sheet: breakthrough therapies. FDA website. https://www.fda.gov/RegulatoryInformation/LawsEnforcedbyFDA/SignificantAmendmentstotheFDCAct/FDASIA/ucm329491.htm. Published December 10, 2014. Accessed April 14, 2016.
25. Buffery D. The 2015 oncology drug pipeline: innovation drives the race to cure cancer. Am Health Drug Benefits. 2015;8(4):216-222.
26. Hoffman J. Pipeline series: renal cell carcinoma. Cancer Therapy Advisor website. http://www.cancertherapyadvisor.com/renal-cell-carcinoma/renal-cell-carcinoma-rcc-drug-pipeline/article/484189/3/. Published March 18, 2016. Accessed May 17, 2016.
27. Nuijten M, Renkens M, Kogels E. The decision-making process of payers: a pilot survey in the Netherlands. ISPOR Connections. 2011;17(5):8-9. https://www.ispor.org/news/articles/July-Aug2011/Decision-Making-Process-of-Payers-A-Pilot-Survey-In-Netherlands.asp.
28. Key statistics for lung cancer. American Cancer Society website. https://www.cancer.org/cancer/non-small-cell-lung-cancer/about/key-statistics.html. Updated January 5, 2017. Accessed September 14, 2017.
29. 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. Am J Manag Care. 2012;18(suppl 11):S265-S271.
30. Thornton Snider J, Romley JA, Vogt WB, Philipson TJ. The option value of innovation. Forum Health Econ Policy. 2012;15(2). doi: 10.1515/1558-9544.1306.
31. Surveillance, Epidemiology, and End Results (SEER) Program Database. National Cancer Institute, ed. Washington, DC 2011. https://seer.cancer.gov/data/. Accessed October 1, 2015.
32. Overview of the SEER program. National Cancer Institute website. https://seer.cancer.gov/about/overview.html. Published 2017. Accessed September 14, 2017.
33. The Human Mortality Database. http://www.mortality.org/. Accessed 2015.
34. FDA expands approved use of Opdivo to treat lung cancer [news release]. Silver Spring, MD: FDA; March 4, 2015. http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm436534.htm. Accessed April 3, 2015.
35. Motzer RJ, Rini BI, McDermott DF, et al. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol. 2015;33(13):1430-1437. doi: 10.1200/JCO.2014.59.0703.
36. Paz-Ares L, Horn L, Borghaei H, et al. Phase III, randomized trial (CheckMate 057) of nivolumab (NIVO) versus docetaxel (DOC) in advanced non-squamous cell (non-SQ) non-small cell lung cancer (NSCLC). J Clin Oncol. 2015;33(suppl; abstr LBA109). http://meetinglibrary.asco.org/record/115609/abstract.
37. Nivolumab full prescribing information. FDA website. https://www.accessdata.fda.gov/drugsatfda_docs/label/2016/125554s019lbl.pdf. Published 2015. Accessed September 14, 2017.
38. Edge S, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A, eds. AJCC Cancer Staging Manual. 7th ed. New York, NY: Springer; 2010.
39. Lee R, Miller T. Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography. 2001;38(4):537-549.
40. Lee RD, Carter LR. Modeling and forecasting U.S. mortality. J Am Stat Assoc. 1992;87(419):659-671. doi: 10.2307/2290201.
41. Hirth RA, Chernew ME, Miller E, Fendrick AM, Weissert WG. Willingness to pay for a quality-adjusted life year: in search of a standard. Med Decis Making. 2000;20(3):332-342. doi: 10.1177/0272989X0002000310.
42. Viscusi WK, Aldy JE. The value of a statistical life: a critical review of market estimates throughout the world. J Risk Uncertain. 2003;27:5-76.
43. Cancer facts and figures 2015. American Cancer Society website. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2015/cancer-facts-and-figures-2015.pdf. Published 2015. Accessed March 25, 2016.
44. Ljungberg B, Campbell SC, Choi HY, et al. The epidemiology of renal cell carcinoma. Eur Urol. 2011;60(1):615-621. doi: 10.1016/j.eururo.2011.06.049.
45. SEER Cancer Statistics Review 1975-2012. National Cancer Institute website. https://seer.cancer.gov/archive/csr/1975_2012/browse_csr.php?sectionSEL=15&pageSEL=sect_15_table.08.html. Published 2016. Accessed April 14, 2016.
46. Protzel C, Maruschke M, Hakenberg OW. Epidemiology, aetiology, and pathogenesis of renal cell carcinoma. Eur Urol Suppl. 2012;11(3):52-59. doi: 10.1016/j.eursup.2012.05.002.
47. Howden LM, Meyer JA. Age and sex composition: 2010: 2010 Census Briefs. US Census Bureau website. http://www.census.gov/prod/cen2010/briefs/c2010br-03.pdf. Published May 2011. Accessed May 17, 2016.
48. Whitehead SJ, Ali S. Health outcomes in economic evaluation: the QALY and utilities. Br Med Bull. 2010;96(1):5-21. doi: 10.1093/bmb/ldq033.