The Value of Survival Gains in Myelodysplastic Syndromes

January 16, 2017
Joanna P. MacEwan, PhD
Joanna P. MacEwan, PhD

,
Wes Yin, PhD
Wes Yin, PhD

,
Satyin Kaura, MSci, MBA
Satyin Kaura, MSci, MBA

,
Zeba M. Khan, PhD
Zeba M. Khan, PhD

Volume 23, Issue 1

This study measured the value of survival gains attributable to the introduction of 3 novel therapies for myelodysplastic syndromes.

ABSTRACT

Objectives: To measure the value of survival gains attributable to the introduction of 3 novel therapies for myelodysplastic syndromes (MDS).

Study Design: Retrospective study of patients diagnosed with MDS in the Surveillance, Epidemiology and End Results Program (SEER) registry, clinical trial evidence for MDS therapies, and claims data.

Methods: We used multivariate Cox proportional hazards models to estimate the increase in survival associated with the introduction of the 3 new therapies for patients diagnosed with MDS from 2001 to 2011 in the SEER cancer registry. Increases in survival associated with the 3 novel therapies were estimated using retrospective survival analyses and published clinical trial evidence. MDS treatment costs were estimated using Ingenix claims data and used to calculate the share of the value of survival gains retained by patients.

Results: We estimated that the introduction of these 3 therapies is associated with a hazard ratio of 0.901 (P <.10), and a 73% increase in median survival from 33 to 57 months. We estimated that for current and future MDS patients, these 3 therapies will generate over $193 billion in cumulative value through extensions in patient survival.

Conclusions: This study demonstrates that the value of recently approved innovative therapies in MDS is large and that the value of survival gains in MDS far outweighs their costs.

Am J Manag Care. 2017;23(1):e10-e15

Takeaway Points

  • Innovative treatments for myelodysplastic syndromes (MDS) have generated significant benefits for patients by markedly extending survival.
  • The value of survival gains far outweighs their cost, suggesting that improving patient access to these MDS therapies is warranted.
  • The value of survival gains represents a conservative estimate of the total value of innovative treatments for MDS to society, which also includes the value of medical cost offsets, productivity gains, and other sources of value, such as reduced caregiver burden.

Over the past several decades, innovation in both the detection and treatment of cancer has resulted in improved survival rates.1,2 Several tumor types—including breast,3 colon,4 chronic myeloid leukemia (CML),5,6 multiple myeloma,7 and non-Hodgkin lymphoma8—have experienced dramatic gains in survival, due in part to the introduction of targeted therapies, such as tyrosine kinase inhibitors (TKIs), vascular endothelial growth factor (VEGF) targeted drugs, immunomodulators (IMiDs), and proteasome inhibitors, among others.9,10 In contrast to conventional chemotherapies that kill rapidly dividing cells—whether cancerous or not—targeted therapies inhibit cancer cell growth and improve survival by interfering with specific molecular targets.11

Several clinical trials investigating novel targeted therapies for myelodysplastic syndromes (MDS) have demonstrated improvement in overall survival (OS) and progression-free survival (PFS).12,13 For example, azacitidine has been shown to successfully improve OS and quality of life,14 increasing median OS by nearly 10 months, while reducing the risk of fatal complications.13 Similarly, patients with MDS being treated with decitabine exhibited better overall response and PFS, and patients responsive to lenalidomide had a significantly lower risk of death or progression to acute myeloid leukemia (AML) after 6 months, compared with patients with MDS being treated with a placebo.15-17

The existing literature demonstrates that the return on investment in advances in treatments for cancer and acute myocardial infarction, among others, has been large, but may not be constant over time.18-20 This study contributes to the larger debate surrounding the value of medical innovation and the need for studying value in specific disease, time, and geographic contexts, given ongoing movement toward linking reimbursement to value. The introduction of novel targeted therapies, in MDS and elsewhere, coincides with increased scrutiny toward the relative value and cost of cancer therapies more generally.21-23 This attention motivates rigorous evaluation of the economic value of new cancer therapies, where value captures improved survival, improved quality of life, reduced caregiver burden, as well as other dimensions of value.24 In one recent analysis of CML, Yin et al (2012) measured the value generated by new novel targeted therapies along 1 dimension of value: survival gains. They estimated that the survival gains attributed to the introduction of TKIs generated significant social value.25 Similarly, studies on the value of improvements in cancer survival and mortality find that these gains have significant societal value.26,27

For MDS, to our knowledge, there are currently no analyses that measure the value of survival gains attributable to innovative therapies relative to their cost.28 Moreover, no study has measured MDS survival gains in the real world, where benefits may differ from those observed in clinical trials. This study addresses this gap in the literature. We estimated the value of survival gains in MDS from the introduction of 3 targeted therapies: azacitidine, decitabine, and lenalidomide. Following the analytical framework applied in Yin et al (2012),25 this study used data from the Surveillance, Epidemiology, and End Results Program (SEER) cancer registry database for 2001 to 2011, to estimate survival gains in the real world as a result of the introduction of these 3 therapies. We then calibrated an existing economic model to value the estimated increases in survival.29 Finally, we used the Ingenix Touchstone commercial claims database (Ingenix) to estimate treatment costs, which we compared with the total value of survival gains.

METHODSSurvival Analysis

We conducted 3 distinct subanalyses. In the first, we used SEER registry data from 2001 to 2011 for patients with MDS, and Cox proportional hazards models to estimate the increase in survival in the real world due to the introductions of key new therapies for MDS between 2001 and 2011. The primary independent variables in the survival models were the binary variables that indicated when new therapies were introduced (Table 1). Prior to 2001, the International Classification of Disease classified MDS as a blood disorder rather than a neoplasm, making the identification of MDS (diagnosed prior to 2001 in SEER) problematic. Therefore, we restricted our analysis to patients diagnosed in 2001 or later.

We included a binary independent variable—YEARS2004-2005—that takes a value of 1 in (diagnosis) years 2004 and 2005, and another binary independent variable—YEARSPost2006—that takes a value of 1 in years 2006 to 2011. The regression coefficient on YEARS2004-2005 captures the survival effect of the introduction of azacitidine (introduced in 2004); and the coefficient on YEARSPost2006 captures the effect of the introductions of lenalidomide (approved in late 2005) and decitabine (introduced in 2006), as well the availability of azacitidine. Because all 3 therapies have been available from 2006 on, we were only able to estimate the combined effect of the availability of all 3 therapies for MDS from 2006 to 2011. The hazard ratio (HR) associated with the YEARSPost2006 variable is the primary estimate of the combined effect of new therapies on MDS survival, and is the key input into our economic model of the value of survival gains.

Importantly, in order to interpret the HR associated with the YEARSPost2006 as the combined effect of the 3 therapies, we included a set of flexible time variables to capture the secular trends in survival that are unrelated to the introduction of azacitidine, lenalidomide, and decitabine. In this way, we would not confound the estimated impact of new therapies on survival with these unrelated changes in MDS survival. In our final specification, we included a cubic annual time trend. We also controlled for a rich set of patient demographic characteristics, including age at diagnosis, gender, tumor sequence, and race. This ensured that changes in survival due to changes in case mix over time would not be incorrectly attributed to the impact of new therapies for MDS.

Because SEER lacks information on which specific therapies patients utilize, the hazard model allowed us to estimate the average impact of new therapy introduction on the entire MDS population, including patients undergoing treatment with therapies other than azacitidine, lenalidomide, and decitabine. Hence, our regression estimates captured the “intended to treat” (ITT) effect of the 3 therapies on MDS survival. To estimate the survival impact on those treated by the new therapies (ie, the “treatment on the treated” (TOT) effect), we used the Ingenix claims data to calculate the average share of patients with MDS being treated by the new therapies between 2006 and 2011. We then scaled the ITT effect by the share of patients treated with the novel therapies to calculate the TOT effect.

Value of Survival Gains

To estimate the value of survival gains in MDS, we applied methods developed by Becker, Philipson and Soares (2005),30 which allowed us to estimate the value patients place on increased longevity. This method involved calibrating an economic model to calculate the utility of patients with cancer when facing the baseline survival curve, as well as the utility curve when faced with an improved survival curve attributable to new treatments. This model also allowed us to estimate the dollar amount that would make baseline patients with cancer as well off as patients with the improved survival, holding all else equal. Following Becker, Philipson, and Soares (2005), we interpreted this dollar amount as the value of the survival gain.

Because utility in this framework depends on the value of consumption while alive, this dollar amount depends on patient income levels, which, at a very general level, dictates patients’ ability to consume goods, including healthcare. The model was therefore calibrated to the income level of patients with cancer, in addition to discount rates, marginal utility of consumption, and other standard economic parameters, as summarized in Becker, Philipson, and Soares (2005) (see eAppendix for further details [available at ajmc.com]).30

The economic model takes as inputs the estimated survival gains, as well as patient “full” income levels, as the total value of time available for work and leisure.31 We estimated patient income using the Medical Expenditure Panel Survey (MEPS) data from 2010 and 2011.32 However, MEPS did not have a sufficient number of patients with MDS (who fall into the “other” cancer category) to allow for the estimation of tumor-specific estimates of income; therefore, to produce reliable estimates of annual income, we estimated the expected annual earnings and full income of all patients with cancer, 18 years or older, assuming earned income reflects 8 hours of work per day and, therefore, full income equals double earned income.

We estimated the annual treatment costs of these 3 therapies using claims data from the Ingenix database for years 2006 to 2012, averaging across years and inflating to 2013 dollars using the Bureau of Labor Statistics Consumer Price Index.33 The Ingenix database is a private sector health insurance claims—based database that combines information from a wide array of different insurance providers. The database captures all healthcare claims, including prescription drugs, and inpatient, emergency, and ambulatory services for elderly and nonelderly individuals with employer-provided insurance from more than 50 large Fortune 500 firms. We calculated the present discounted value (PDV) of the increase in survival for a single patient over his or her lifetime, where value and costs in later years are weighted by each year’s estimated survival probability. Finally, we calculated the PDV of the lifetime value and costs for all future patient cohorts.

The total PDV of the lifetime survival gains and costs considers future MDS incident cohorts together with the current cohort, assuming a 3% discount rate, and accounts for patent expiry: generic versions of azacitidine and decitabine became available in 2013, and the lenalidomide patent will expire in 2027. How much prices decline following patent expiry depends on several factors, including the number of generic manufacturers that enter the market, the complexity of producing the molecule, outpatient rather than pharmacy benefit coverage, branded-drug market share, the number of competing therapies, and the length of time since patent expiry.34 For these reasons, the effect of patent expiry on the price of specialty drugs like oncologics may be different than the effect of expiry on nonspecialty drugs. To accommodate for this uncertainty in post patent expiry drug prices, we used a conservative estimate of the effect of patent expiration on drug prices. A recent study estimates that intravenously administered specialty drugs decline by 34% after patent expiry, and that oral specialty drugs decline by 21% after patent expiry.34 We used these price declines when forecasting future costs of generic azacitidine, lenalidomide, and decitabine treatment in MDS.

RESULTSSummary Statistics

The sample of patients diagnosed with MDS between 2001 and 2011 from the SEER cancer registry database contained 38,025 individuals. Descriptive statistics for the study sample are displayed in Table 2. Sixty percent of these patients were diagnosed in 2006 or later; patients diagnosed in 2006 or later were slightly older at diagnosis and less likely to be white, but overall, the 2 populations had very similar characteristics.

Change in MDS Survival Curves Attributable to New Therapies

The coefficient on the post 2006 indicator reflected the decline in the hazard rate associated with the introduction of the 3 therapies relative to the hazard rate in years prior to their introduction (2001-2003). In our preferred model, which included more flexible controls for secular trends in survival, we estimated that the introduction of these 3 therapies is associated with an HR of 0.901 (P <.10; see column 2 of Table 3). That is, we estimated that patients with MDS diagnosed in 2006 or later had an approximately 10% lower chance of dying within the year (conditional on surviving to that year) than patients diagnosed in 2006, but under the counterfactual that the 3 new therapies were not introduced (Table 3). Note that the counterfactual survival rate in 2006 is the estimated survival rate in the pre-introduction (2003) period, adjusted for the secular increase in survival between 2003 and 2006.

Approximately one-fourth of patients with MDS had claims for at least 1 of the 3 novel treatments consistently over this period, resulting in an overall average increase from 33 to 57.5 months in median survival for treated individuals (Figure).

Societal Value of Survival Gains Attributable to New Therapies

The annual value of survival gains associated with innovative treatments—roughly $208,000 per year—equals the estimated amount a patient would pay for the higher survival profile associated with the new therapies. Note that this is conservative compared with the value of a 24-month (from 33 to 57.5 months) increase in median survival, as implied by conventional health technology assessment methods, which would range from $400,000 to $600,000 depending on the assumed valued of a statistical life-year.35 These differences stem from diminishing utility in longevity, which appeared in the model in 2 places: 1) the size of the survival gain and 2) the baseline survival prior to the introduction of the new therapies. If either of these are small, the marginal utility, and hence, valuation, of an incremental survival gain is larger than for the same incremental survival when the gain is large or when the baseline survival is already large.

Table 4 compares the estimated value of survival gains to costs, where the annual treatment costs of the new therapies,Author Affiliations: Precision Health Economics (JPM), Los Angeles, CA; University of California, Los Angeles (WY), Los Angeles, CA; Celgene Corporation (SK, ZMK), Summit, NJ

Source of Funding: Celgene Co.

Author Disclosures: Dr MacEwan is an employee of Precision Health Economics, a research consulting firm owned by Precision Medicine Group, which was compensated by Celgene to perform the study. Dr Yin is a consultant to Precision Health Economics. Dr Kahn and Mr Kaura are employees and stockholders of Celgene, which manufactures a product to treat MDS.

Authorship Information: Concept and design (WY, JPM, SK, ZMK); acquisition of data (WY, JPM); analysis and interpretation of data (WY, JPM, SK, ZMK); drafting of the manuscript (WY, JPM, SK, ZMK); critical revision of the manuscript for important intellectual content (WY, JPM, SK, ZMK); statistical analysis (JPM); obtaining funding (SK, ZMK); administrative, technical, or logistic support (WY, JPM); and supervision (WY, ZMK).

Address Correspondence to: Joanna P. MacEwan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Los Angeles, CA 90025. E-mail: Joanna.MacEwan@pheconomics.com.

REFERENCES

1. Breen N, Wagener DK, Brown ML, Davis WW, Ballard-Barbash R. Progress in cancer screening over a decade: results of cancer screening from the 1987, 1992, and 1998 National Health Interview Surveys. J Natl Cancer Inst. 2001;93(22):1704-1713.

2. Espey DK, Wu XC, Swan J, et al. Annual report to the nation on the status of cancer, 1975-2004, featuring cancer in American Indians and Alaska Natives. Cancer. 2007;110(10):2119-2152.

3. Cobleigh MA, Vogel CL, Tripathy D, et al. Multinational study of the efficacy and safety of humanized anti-HER2 monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic disease. J Clin Oncol. 1999;17(9):2639-2648.

4. American Society of Clinical Oncology. Progress and timeline: colorectal cancer. Cancer Progress website. http://www.cancerprogress.net/sites/cancerprogress.net/files/category-downloads/progress_against_colorectal_cancer_timeline.pdf. Published 2015. Accessed August 31, 2014.

5. O’Brien SG, Guilhot F, Larson RA, et al; IRIS Investigators. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med. 2003;348(11):994-1004.

6. Druker BJ, Guilhot F, O’Brien SG, et al; IRIS Investigators. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med. 2006;355(23):2408-2417.

7. SEER stat fact sheets: myeloma. National Cancer Institute website. http://seer.cancer.gov/statfacts/html/mulmy.html. Accessed September 15, 2015.

8. American Society of Clinical Oncology. Progress and timelines: progress against lymphoma. CancerProgress.net website. http://www.cancerprogress.net/sites/cancerprogress.net/files/category-downloads/progress_against_lymphoma_timeline.pdf. Accessed August 31, 2014.

9. Pulte D, Gondos A, Brenner H. Ongoing improvement in outcomes for patients diagnosed as having Non-Hodgkin lymphoma from the 1990s to the early 21st century. Arch Intern Med. 2008;168(5):469-476. doi: 10.1001/archinternmed.2007.125.

10. American Society of Clinical Oncology. Progress and timeline: targeted therapies. CancerProgress.net website. http://www.cancerprogress.net/timeline/targeted-drugs. Accessed August 31, 2014.

11. Targeted cancer therapies. National Cancer Institute website. https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/targeted-therapies-fact-sheet. Published April 25, 2014. Accessed September 9, 2014.

12. Jädersten M, Malcovati L, Dybedal I, et al. Erythropoietin and granulocyte-colony stimulating factor treatment associated with improved survival in myelodysplastic syndrome. J Clin Oncol. 2008;26(21):3607-3613. doi: 10.1200/JCO.2007.15.4906.

13. Silverman LR, Demakos EP, Peterson BL, et al. Randomized controlled trial of azacitidine in patients with the myelodysplastic syndrome: a study of the cancer and leukemia group B. J Clin Oncol. 2002;20(10):2429-2440.

14. Fenaux P, Mufti GJ, Hellstrom-Lindberg E, et al; International Vidaza High-Risk MDS Survival Study Group. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol. 2009;10(3):223-232. doi: 10.1016/S1470-2045(09)70003-8.

15. Kantarjian H, Issa JPJ, Rosenfeld CS, et al. Decitabine improves patient outcomes in myelodysplastic syndromes: results of a phase III randomized study. Cancer. 2006;106(8):1794-1803.

16. Lübbert M, Suciu S, Baila L, et al. Low-dose decitabine versus best supportive care in elderly patients with intermediate- or high-risk myelodysplastic syndrome (MDS) ineligible for intensive chemotherapy: final results of the randomized phase III study of the European Organisation for Research and Treatment of Cancer Leukemia Group and the German MDS Study Group. J Clin Oncol. 2011;29(15):1987-1996. doi: 10.1200/JCO.2010.30.9245.

17. Fenaux P, Giagounidis A, Selleslag D, et al; MDS-004 Lenalidomide del5q Study Group. A randomized phase 3 study of lenalidomide versus placebo in RBC transfusion-dependent patients with low-/intermediate-1-risk myelodysplastic syndromes with del5q. Blood. 2011;118(14):3765-3776. doi: 10.1182/blood-2011-01-330126.

18. 2013 economic report of the President. The White House website. https://www.whitehouse.gov/administration/eop/cea/economic-report-of-the-President/2013. Accessed September 9, 2014.

19. Cutler DM. Declining disability among the elderly. Health Aff (Millwood). 2001;20(6):11-27.

20. Skinner JS, Staiger DO, Fisher ES. Is technological change in medicine always worth it? the case of acute myocardial infarction. Health Aff (Millwood). 2006;25(2):w34-w47.

21. Experts in chronic myeloid leukemia. the price of drugs for chronic myeloid leukemia (CML) is a reflection of the unsustainable prices of cancer drugs: from the perspective of a large group of CML experts. Blood. 2013;121(22):4439-4442. doi: 10.1182/blood-2013-03-490003.

22. Schnipper LE, Davidson NE, Wollins DS, et al. 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.

23. Tefferi A, Kantarjian H, Rajkumar SV, et al. In support of a patient-driven initiative and petition to lower the high price of cancer drugs. Paper presented at: Mayo Clinic Proceedings, August 2015; 90(8):996—1000. http://dx.doi.org/10.1016/j.mayocp.2015.06.001.

24. Goldman D, Lakdawalla D, Philipson TJ, Yin W. Valuing health technologies at NICE: recommendations for improved incorporation of treatment value in HTA. Health Econ. 2010;19(10):1109-1116. doi: 10.1002/hec.1654.

25. Yin W, Penrod JR, Maclean R, Lakdawalla DN, Philipson T. Value of survival gains in chronic myeloid leukemia. Am J Manag Care. 2012;18(suppl 11):S257-S264.

26. Chandra A, MacEwan JP, Campinha-Bacote A, Khan ZM. Returns to society from investment in cancer research and development. Forum Health Econ Policy. 2016;19(1):71-86. doi: 10.1515/fhep-2014-0022.

27. Lakdawalla DN, Sun EC, Jena AB, Reyes CM, Goldman DP, Philipson TJ. An economic evaluation of the war on cancer. J Health Econ. 2010;29(3):333-346. doi: 10.1016/j.jhealeco.2010.02.006.

28. Faguet GB. The War on Cancer. Dordrecht, The Netherlands: Springer; 2005.

29. Becker GS, Philipson TJ, Soares RR. The quantity and quality of life and the evolution of world inequality. National Bureau of Economic Research website. http://www.nber.org/papers/w9765. Published June 2003. Accessed August 30, 2014.

30. Becker GS, Philipson TJ, Soares RR. The quantity and quality of life and the evolution of world inequality. Am Econ Rev. 2005;95(1):277-291.

31. Becker GS. A Theory of the Allocation of Time. Economic Journal. 1965;75:493--517.

32. Medical Expenditure Panel Survey [2010]. Agency for Healthcare Research and Quality website. http://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp. Accessed May 14, 2014.

33. Consumer price index-all urban consumers 2015. Department of Labor Bureau of Labor Statistics website. http://data.bls.gov/cgi-bin/surveymost?cu. Accessed February 23, 2015.

34. Conti RM, Berndt ER. Specialty drug prices and utilization after loss of U.S. patent exclusivity, 2001-2007. National Bureau of Economic Research website. http://www.nber.org/papers/w20016. Published March 2014. Accessed December 2016.

35. Aldy JE, Viscusi WK. Age differences in the value of statistical life: revealed preference evidence. Rev Environ Econ Policy. 2007;1(2):241-260. doi: 10.1093/reep/rem014.

36. Fast stats [age-adjusted SEER incidence rates by cancer site all ages, all races, both sexes 2000-2011]. National Cancer Institute website. http://seer.cancer.gov/faststats/selections.php?series=cancer. Accessed December 2016.

37. Population estimates—state totals: vintage 2011. US Census Bureau website. http://www.census.gov/popest/data/state/totals/2011/. Accessed September 10, 2014.

38. Bejar R, Steensma DP. Recent developments in myelodysplastic syndromes. Blood. 2014;124(18):2793-2803. doi: 10.1182/blood-2014-04-522136.

39. Steensma DP. The changing classification of myelodysplastic syndromes: what’s in a name? Hematology Am Soc Hematol Educ Program. 2009;2009(1):645-655. doi: 10.1182/asheducation-2009.1.645.&ensp;