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Dynamic Cost-Effectiveness of Oncology Drugs
Yang Lu, PhD; John R. Penrod, PhD; Neeraj Sood, PhD; Saarah Woodby, BA; and Tomas Philipson, PhD
Value of Survival Gains in Chronic Myeloid Leukemia
Wesley Yin, PhD; John R. Penrod, PhD; J. Ross Maclean, MD; Darius N. Lakdawalla, PhD; and Tomas Philipson, PhD
Appendix A -- Value of Survival Gains in Chronic Myeloid Leukemia
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The Option Value of Innovative Treatments in the Context of Chronic Myeloid Leukemia
Yuri Sanchez, PhD; John R. Penrod, PhD; Xiaoli Lily Qiu, PhD; John Romley, PhD; Julia Thornton Snider, PhD; and Tomas Philipson, PhD
eAppendix -- New Approaches to Measuring Value in Oncology Therapy
Participating Faculty: New Approaches to Measuring Value in Oncology Therapy

The Option Value of Innovative Treatments in the Context of Chronic Myeloid Leukemia

Yuri Sanchez, PhD; John R. Penrod, PhD; Xiaoli Lily Qiu, PhD; John Romley, PhD; Julia Thornton Snider, PhD; and Tomas Philipson, PhD
Objective: To quantify in the context of chronic myeloid leukemia (CML) the additional value patients receive when innovative treatments enable them to survive until the advent of even more effective future treatments (ie, the “option value”).

Study Design: Observational study using data from the Surveillance, Epidemiology and End Results (SEER) cancer registry comprising all US patients with CML diagnosed between 2000 and 2008 (N = 9,760).

Methods: We quantified the option value of recent breakthroughs in CML treatment by first conducting retrospective survival analyses on SEER data to assess the effectiveness of TKI treatments, and then forecasting survival from CML and other causes to measure expected future medical progress. We then developed an analytical framework to calculate option value of innovative CML therapies, and used an economic model to value these gains. We calculated the option value created both by future innovations in CML treatment and by medical progress in reducing background mortality.

Results: For a recently diagnosed CML patient, the option value of innovative therapies from future medical innovation amounts to 0.76 life-years. This option value is worth $63,000, equivalent to 9% of the average survival gains from existing treatments. Future innovations in CML treatment jointly account for 96% of this benefit.

Conclusions: The option value of innovative treatments has significance in the context of CML and, more broadly, in disease areas with rapid innovation. Incorporating option value into traditional valuations of medical innovations is both a feasible and a necessary practice in health technology assessment.

(Am J Manag Care. 2012;18:S265-S271)
Traditional health-technology assessment, such as cost-effectiveness analysis, measures the value of a health innovation by comparing benefits (eg, survival gains or improved quality of life) to costs, assuming no further improvements in medical technology aside from the innovation in question.1 Such methods overlook the fact that life-extending innovations can allow patients to live until the next breakthrough, which may offer additional survival opportunities. This possibility of benefiting from future improvements in medical technology is the “option value” of a health innovation. As long as medical technology continues to advance, there will be option value in survival-improving innovations. Failing to account for this value ignores a potentially important source of benefit to patients, understating the effectiveness of new therapies, especially in areas of rapid innovation.

The existence of option value has been demonstrated in the context of end-of-life care from the perspective of economic theory2 but, with a few exceptions,3 option value has not been incorporated into existing assessments of pharmaceutical innovation. Because the literature lacks both a guide to incorporating option value into health-technology assessment and a method for calculating the option value of current technologies, real-world demonstrations are needed to illustrate the feasibility and necessity of incorporating option value into practice. In the context of healthcare management, option value assessments are key to achieving a better understanding of the overall effectiveness of innovative treatments that may ultimately lead to significant improvements in patient care.

The latest breakthroughs in chronic myelogenous leukemia (CML) treatment serve as a prominent example of the relevance of the option-value concept. (See the eAppendix at for a list of agents used for CML treatment since 1959 and the dates of their approval by the US Food and Drug Administration.) Historically, survival has been 3 to 5 years from the time of CML diagnosis.4 However, the introduction of tyrosine kinase inhibitors (TKIs), an innovative class of targeted therapies, has led to substantial survival gains in the treatment of CML, with overall survival of patients in clinical trials increasing to 89% at 5 years.5 Treatment with the first-generation TKI imatinib rapidly became the standard of care after the drug was introduced as first-line treatment in 2001.6,7 Imatinib remained the only TKI approved for CML treatment for several years until the introduction of 2 second-generation agents. Dasatinib was approved for second-line use in June 2006, representing a breakthrough for CML patients failing imatinib; long-term follow-up of these patients showed that the vast majority (78%) were alive at 5 years.8-14 In 2007, nilotinib was also approved for second-line use.15,16 While dasatinib and nilotinib were initially approved for second-line use, these agents demonstrated superiority to imatinib in the first-line setting and were approved for this indication in 2010.8,17

In this study we quantified the extent to which the firstgeneration TKI served as a “bridge” to subsequent CML treatments, and how treatment with first- and second-generation TKIs is allowing patients to survive into periods of future improvements in CML treatment and background mortality. For this purpose we developed an analytical framework to enable incorporation of option value into healthtechnology assessment. We used a Cox proportional hazards model to estimate historical real-world CML survival trends, and employed the Lee-Carter method to forecast future survival from any cause of death in the Surveillance, Epidemiology and End Results (SEER) cancer registry. Based on these estimates we quantified the option value of innovative TKI therapies and used an economic model2,18 to express these gains in dollar terms.


Conceptual Framework

As discussed previously, option value is the additional survival benefit patients receive when innovative treatments enable them to experience future medical breakthroughs; stated more simply, it is the benefit derived from the opportunity to live longer. Measuring the option value of a treatment is feasible to the extent that there are available data on treatment effectiveness and future (post-treatment) survival. As an illustration of this point, assume that half of the patients suffering from a terminal disease experience a 1-year life extension due to innovative Therapy A, while the remaining half do not survive; hence, the life expectancy gain from Therapy A is 0.5 life-years. Next, suppose that, after 1 year, surviving patients gain access to a new medical innovation (Therapy B) that allows them to live an additional 4 months (ie, 0.33 life-years = 4/12); thus, the estimated option value of Therapy A is 2 months (or 0.16 life-years, computed as the product of 0.5 life-years from Therapy A effectiveness and 0.33 life-years from future access to Therapy B). A more detailed explanation of this concept is presented in the eAppendix, along with the analytical framework to assess the value of a health innovation and incorporate its option value.

In the context of CML, initial assessments of benefits comparing TKI therapy with the previous standard of care underestimate the value of TKI therapy by ignoring its option value. In particular, the option value of first-line treatment includes: 1) reductions in background mortality through future medical innovations, and 2) survival improvements that enable patients who develop treatment resistance to survive with greater probability into a period during which second-generation agents become available. These benefits have not been previously estimated.


SEER is the gold standard registry for cancer in the United States. SEER registries identify incident cases of malignancies based on uniform reporting, according to the International Classification of Disease for Oncology, 3rd Edition (ICD-03). As the only national cancer database that follows patients over time to trace survival, SEER contains information on 21,558 individuals diagnosed with CML between 1973 and 2008. The data set includes type(s) of cancer (up to 10 tumors), age at diagnosis, year of diagnosis, gender, race, ethnicity, marital status, county of residence, and time and cause of death.

Patients with CML were identified in the SEER registry based on ICD-03 codes 9863 (chronic myeloid/myelogenous leukemia), 9875 (chronic myelogenous leukemia, BCR/ ABL-positive), and 9876 (atypical chronic myeloid leukemia, BCR/ABL-negative). Because imatinib was introduced in 2001, we restricted the sample to CML patients diagnosed in 2000 or later. Patients diagnosed in 2000 served as the control (pre-treatment) group in order to measure the cost of delaying treatment by 1 year. Table 1 presents summary statistics for the restricted sample; specifically, for patients diagnosed between 2001 and 2005 (the time period when firstgeneration TKI treatment became available) and for patients diagnosed after 2006 (the time period when second-generation treatments became available). As a sensitivity analysis, we considered control groups diagnosed with CML up to 5 years prior to 2000. The results are quantitatively similar and shown in the eAppendix. The 2000 to 2008 sample consists primarily of white (82%) and male (58%) patients. More than half (52%) of the patients are married, and the average age at diagnosis was 62 years. Approximately 5 of every 6 patients have only 1 primary cancer (CML), and the average mortality from CML and other causes in the period of analysis is 26% and 11%, respectively. Aside from mortality, patient characteristics are similar across control and treatment groups. Mortality from CML was 25% lower among patients diagnosed with CML between 2001 and 2005, relative to patients diagnosed before the introduction of firstgeneration treatment. Other-cause mortality, or mortality due to causes different from CML, is similar across the firstgeneration treatment and control groups. Additionally, both CML and other-cause mortality are substantially smaller among patients diagnosed between 2006 and 2008 relative to patients diagnosed before 2005. This is driven largely by a shorter follow-up period for the second-generation treatment group, resulting in greater censoring of CML and other-cause mortality.

Statistical Analysis

We combined 2 distinct statistical analyses to compute the option value of TKIs. First, we employed a Cox proportional hazards model19 to calculate past improvements in CML survival and survival from diseases different from CML between 2000 and 2008. Second, we used the Lee-Carter method to forecast future improvements in CML and othercause mortality several decades into the future.

We estimated the direct survival gains from TKIs by first applying the Cox model to the SEER sample of patients diagnosed with CML between 2000 and 2008. We included year of diagnosis as the key explanatory variable to generate year-specific survival curves. To mitigate potential bias from unobservable factors, we controlled for a rich set of patient characteristics that affect mortality independently of year of diagnosis, including age at the time of CML diagnosis, month of CML diagnosis, number of other cancers at diagnosis, and other demographic variables. The eAppendix describes the Cox proportional hazards model and shows the regression estimates. Next, we developed an algebraic method to identify survival associated with first- or secondline TKI treatment from the year-specific survival curves observed between 2000 and 2008. This algebraic method is explained in the eAppendix. Year-specific survival curves were weighted by population survival probabilities in the United States from the Human Mortality Database (HMD) to obtain nationally representative survival data of the CML population. HMD life tables are based on population estimates from the US Census Bureau, and birth and death reporting from the Census Bureau and the National Center for Health Statistics. We accounted for treatment resistance by incorporating estimates of the probability of resistance to first-line treatment from the existing literature.20 Given the sample’s limited time span, survival past 7 years from diagnosis was assumed to decline at the rate of the general population based on the HMD. A sensitivity test to evaluate the implications of this assumption is presented in the Discussion section.

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