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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
Although clinical trial data have quantified patient survival gains associated with tyrosine kinase inhibitors in chronic myeloid leukemia, the overall value of these benefits is unknown.
Although clinical trial data have quantified patient survival gains associated with tyrosine kinase inhibitors in chronic myeloid leukemia, the overall value of these benefits is unknown.

Objective: To estimate the total value of survival gains associated with first- and second-line TKI therapy in chronic myeloid leukemia (CML) and the fraction of tyrosine kinase inhibitor (TKI)- related survival-gain value retained by patients and drug companies.

Study Design: This retrospective study identified CML patient data from the Surveillance, Epidemiology and End Results registry, dasatinib clinical trials, and insurance claims data sets.

Methods: Multivariate Cox proportional hazard models were used to estimate improvements in CML survival associated with the introduction of first-line imatinib therapy. Survival gains associated with second-line dasatinib treatment were identified via retrospective analyses and published clinical outcomes. An economic model was developed to calculate the social value of survival gains derived from first- and second-line TKI treatment. TKI costs were used to estimate the fraction of survival gain value retained by patients and drug companies.

Results: The introduction of TKIs in 2001 was associated with a hazard ratio of 0.833 (P <.01). Cost analyses indicate that the TKI drug class in CML therapy has created more than $143 billion in social value. Approximately 90% of this value is retained by patients and society, while approximately 10% is recouped by drug companies.

Conclusions: These estimates indicate that the introduction of TKI drugs to treat CML has generated significant social value as a result of survival gains, the vast majority of which has accrued to patients.

(Am J Manag Care. 2012;18:S257-S264)
Cancer chemotherapy has long relied on generalized and nonspecific treatments that often involve substantial patient toxicity. One of the major chemotherapeutic advances over the past decade has been the discovery and deployment of targeted therapies directed at cancer-specific molecules and signaling pathways. In some cases this treatment specificity has proved very effective, appreciably improving survival rates for certain cancers.

Considered broadly, there have been substantial increases in cancer survival rates over the past several decades. The 5-year relative survival rate for all cancers diagnosed between 1996 and 2003 was 66%, up from 50% in the mid-1970s.1 Improvements in survival have been particularly dramatic for breast cancer, colon cancer, and non-Hodgkin lymphoma—conditions for which there have been important advances in therapy, screening, or both.2-4 It has, however, also been argued that some new cancer therapies only marginally extend life, and at a high cost.5,6 Hence, the cost-effectiveness of expensive new cancer therapies has been called into question, with recommendations made for more comprehensive economic evaluations, as well as innovative approaches that consider the unique economic impact of cancer treatments.7-11 The results of the recommended comprehensive economic analyses have important implications for the assessment of managed care, as they promote a better understanding of the total value of medical technologies.

The study described in this article examines the value of survival gains in chronic myeloid leukemia (CML) achieved by treatment with a new class of targeted therapy, tyrosine kinase inhibitors (TKIs), in first- and second-line therapy. In one multicenter, international, open-label, phase 3 randomized study, investigators compared the TKI imatinib with interferon as first-line treatment for CML, and demonstrated an 18-month survival rate of 97.2% for patients treated with imatinib.12 These results were durable; at 60-month follow-up, the overall survival (OS) rate for imatinib-treated patients was 89% (95% confidence interval, 86-92),13 and 7-year event-free survival and OS rates were 81% and 86%, respectively.12 Two newer TKIs, dasatinib and nilotinib, have also been approved for first-line CML treatment.14-16

Studies of second-line TKI therapy in CML have also demonstrated efficacy. Patients receiving the newer TKIs (dasatinib and nilotinib) after imatinib failure experience significant cytogenetic response,17-19 and can expect substantial survival benefits. Indeed, in a randomized, international, multicenter, open-label phase 3 trial of dasatinib as second-line treatment following imatinib failure or intolerance, the overall 60-month survival rate was 78%.20

Based on these clinical trial data, survival gains for CML patients treated with TKIs appear to be substantial. However, survival gains experienced in the community have not been studied, and the overall value of these survival gains—both in aggregate and relative to the cost of treatment—is unknown. In addition, the value of survival gains subsequently returned to patients and society has not been adequately evaluated. Prior research by Sun and colleagues11 revealed that, between 1988 and 2000, a substantial majority (~80%) of US cancerrelated survival gains were due to treatment improvements (as opposed to cancer detection strategies). Subsequently, Lakdawalla and colleagues21 evaluated, from a societal perspective, the economic gains due to cancer treatment associated with these improved overall survival rates; the study used an economic model developed by Becker et al,22 which was designed to assess patients’ willingness to pay for improved survival rates achieved by cancer treatment. Predicated on this model, the estimated average increase in life expectancy of 4 years between 1988 and 2000 was associated with 23 million life-years and $1.9 trillion gained in social value. The model also demonstrated that drug companies acquired 5% to 19% of this total value as revenue.21

In this article we apply a similar approach to estimate the social value of TKIs in the treatment of CML. Observational data and clinical trial results were used to assess survival gains stemming from the introduction of both first- and second-line TKI therapies. Subsequently, an economic framework was employed to calculate the social value of improvements in survival gains due to TKI treatment. Lastly, to estimate the fraction of TKI treatment value retained by patients and society, as well as the proportion recouped by drug companies over time, the cost of TKI treatment was estimated from lifetime individual and population-level perspectives.

Methods

Survival Data


Multivariate Cox proportional hazard models were used to estimate survival improvements in CML associated with the 2001 introduction of first-line imatinib therapy. CML patient survival data were obtained from the Surveillance, Epidemiology, and End Results (SEER) registry. The SEER registry has tracked patients diagnosed with CML from 1973 to 2006 (the year of the most recent registry update). The key advantages of the SEER registry are that it is the only national cancer data base that: 1) follows patients over time in order to track survival, and 2) is large enough to contain sufficient sample sizes for survival analysis.

The sample for the survival analysis was restricted to CML patients diagnosed in 2000 or later. This ensured that the study captured the survival impact of the 2001 introduction of imatinib on the incident patient cohort, whose response to TKI therapy best represents the first-line survival effect of TKIs on present and future CML patients. Using multivariate hazard analysis, a rich set of demographic controls was applied, including age and squared age at diagnosis, gender, marital status, and separate indicator variables for white, black, Hispanic, Asian/Pacific Islander, and other races. To account for secular trends in CML survival unrelated to the introduction of imatinib, flexible controls for year and month of diagnosis were also included.

The key variable of interest in the proportional hazard model was the post-TKI indicator variable, which took the value of 1 in all years following 2000 and corresponded to the 2001 introduction of imatinib for first-line CML treatment. We report the estimated hazard ratio (HR) associated with the post-TKI variable, as well as the implied survival rate ratio.

The timing of second-line TKI approvals (2006 US Food and Drug Administration [FDA] approval for dasatinib; 2007 for nilotinib) precluded retrospective survival analysis of these drugs using the SEER registry. We estimated the community survival effect of new TKIs in second-line therapy by interpolation. First, we calculated the clinical survival effect of second-line relative to first-line treatment, which we obtained by dividing the 60-month OS rate in the second-line dasatinib clinical trial by the 60-month OS rate in the imatinib first-line clinical trial. We then applied this relative effectiveness rate of second-line therapy to the community-based survival effects estimated for imatinib in the retrospective SEER-based survival analysis. In performing this interpolation we assumed that the proportional difference in clinical and community survival rates for imatinib was the same as the proportional difference in clinical and community survival rates for dasatinib. Among newer TKIs we focused on dasatinib for this component of the analysis given the availability of recent 60-month survival results,20 which are not yet available for nilotinib.

The survival effects of imatinib were calculated as the average across all CML patients, regardless of whether they received imatinib; this was interpreted as the “intent-totreat” (ITT) effect. To estimate imatinib’s “treatment-onthe- treated” (TOT) effect, the ITT effect was scaled based on the percentage of CML patients who received treatment. The fraction of CML patients who received TKI treatment following its introduction was estimated using Ingenix Touchstone data,23 a database of private-sector healthcare claims, including prescription drug claims and inpatient emergency ambulatory medical claims from approximately 35 Fortune 500 firms. Individuals with CML were identified based on the presence of 2 or more International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) diagnoses (primary, secondary, or tertiary) with medical claims codes of 205.1x, or 1 ICD-9-CM code and TKI therapy use after diagnosis. Based on this data set, between 2001 and 2002, 40% of CML patients received treatment with imatinib; therefore, the TOT effect was calculated as the ITT survival effect divided by 0.4.

Value of Survival Gains

The economic model developed by Becker et al24 was employed to calculate the social value of survival gains due to first- and second-line TKI therapy. Clinical evidence suggests that, relative to TKI therapy, prevailing treatments are associated with relatively rapid mortality in CML, not slight increments in mortality risk. This economic model calculates patients’ willingness to pay (ie, value to patients) for longevity gains, accounting for discrete (rather than marginal) increases in survival probabilities. The technical details regarding the application of this methodological approach are presented in Appendix A.

In keeping with this methodology, the benefit derived by CML patients was calculated based upon survival gains pertaining to TKI treatment and the average income level among CML patients. In particular, patients’ lifetime value was estimated using income data from the 2005 and 2006 Medical Expenditure Panels Study (MEPS). Due to the small number of CML patients in the MEPS, all MEPS cancer patients were used to estimate CML patient income. The estimated value was then aggregated across all CML cohorts to obtain the total social value of survival gains due to TKI treatment.

Costs

The Ingenix Touchstone Data database provided estimates of annual imatinib treatment costs, as well as information on drugs utilized, total drug costs, and patient costsharing information. Because claims-based costs for dasatinib were not available, these costs were estimated via imputation by applying the proportional difference between the manufacturers’ prices of imatinib and dasatinib to the claims-based cost estimates.

As with the lifetime value of TKI treatment, lifetime treatment cost was determined by calculating the present value of annual imatinib treatment costs given the life expectancy implied by the post-TKI survival curve. The total value of TKI therapy was estimated by summing lifetime costs for a CML patient across all CML patients in a cohort, and then summing present discounted lifetime costs across all present and future cohorts. This took into account the decreased costs associated with the patent expiration of imatinib (2015) and dasatinib (2020). It was assumed that current lifetime and total cohort costs prevailed until patent expiration, after which costs were assumed to be zero. In addition, MEPS and SEER data were used to estimate the size of each CML cohort (4,500), of which 30% were expected to be resistant or intolerant to imatinib based on prior evidence in the literature.25

Results

Descriptive Data


 
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