FIRST v4.0 - Financial Impact of Recurrence Score Testing

FIRST v4.0 has been designed to

1. Predict the financial implications to a health care plan of adopting the Oncotype DX^® breast cancer assay for use in women with ER-positive early-stage invasive breast cancer.
2. To permit plans to input their own characteristics and expected use of the Oncotype DX breast cancer assay in determining the expected costs to the plan.

Inputs and data sources

Table 3. Inputs used in FIRST v4.0, separated into 8 categories.

Data to inform the default values for many of the inputs were obtained from published materials, either from systematic review of the peer-reviewed literature or from publicly available data from government sources. The Navigation bar of FIRST v4.0 provides user access to information on source data (Figure 6).

Many of the inputs values are assumed to be plan specific, and the user is permitted to change these inputs to fit their individual plan’s characteristics. Ranges have been provided for several of the variables, designed to reflect the variability found in the published sources.

Results and computations

Table 4. FIRST v4.0 outputs

User interface

There are two default value options that differ in their data sources for the frequency of RS risk groups. Figure 6 below shows the base case inputs given default values in which the RS risk group frequencies are based on a meta-analysis of published literature.^{2, 4, 10, 21-32}

Figure 6. FIRST v4.0: Budget impact model inputs



Showing default values: risk group frequency values from literature meta-analysis, base case.

Choosing Genomic health sales data default values changes the RS risk group frequency values (Input no. 4) to 57%, 32%, and 11% for low, intermediate and high risk groups in node negative patients. For node positive patients, the risk group frequencies would be, in the same order, 59%, 33%, and 8%.

Scenario analysis tools include lower bound, upper bound and breakeven analyses. The lower bound tool button models a scenario in which test utilization (Input no. 2) is 80% and the costs associated with chemotherapy (Input no. 7) are increased by 25%. In the upper bound scenario, test utilization is 50% and the costs associated with chemotherapy are decreased by 25%. Figure 7 below shows the results of the base case, lower bound and upper bound scenario analyses for both default values options in an abbreviated and condensed presentation of the results interface.

Figure 7. FIRST v4.0 interface. Budget impact model results
A. Default risk group fequency values: Literature meta-analysis

B. Default risk group frequency values: Genomic Health sales data

All computations – except for computing cost of recurrence – involve simple algebra. For example, the projected number of women per year with node-negative, ER-positive invasive breast cancer is the product of

1. number of covered lives in the plan
2. age-adjusted incidence of invasive breast cancer

The projected number of women per year to be tested is product of

1. number of covered lives in the plan
2. age-adjusted incidence of invasive breast cancer
3. percent of women to be tested with the Oncotype DX breast cancer assay

The costs associated with change in chemotherapy use are computed as product of

1. net change in chemotherapy use due to the Oncotype DX breast cancer assay
2. associated costs for each cost component (drugs, supportive care, or adverse events)

Estimating the effect of the Oncotype DX breast cancer assay on late costs of recurrence involves somewhat more detailed computations. FIRST v4.0 predicts when recurrence occurs, assigns a cost to that recurrence, and then adjusts the cost to net present value in 2010 US dollars, using a fixed annual discount rate of 3%.

Key findings (Budget impact model)

• Every scenario examined results in a reduction in total costs (Table 5).
• Net reduction of adjuvant chemotherapy use results in savings in the costs of chemotherapy drugs, administration, supportive care, and adverse events. (per patient tested per year)
  • Literature-based RS risk group frequency: $5,603 (range $4,202 to $7,004) savings.
  • GHI sales data: $6,744 (range $5,058 to $8,430) savings.
• The humanistic benefits of avoiding adjuvant chemotherapy include preventing early toxicities (nausea and vomiting, alopecia) and late toxicities (development of new primary tumors, ovarian failure, and cognitive dysfunction).

Table 5. Scenarios results summary for a 2 million member plan

References

2. Albain KS, Barlow WE, Shak S, et al. Prognostic and predictive value of the 21-gene recurrence score^® assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 2010;11:55-65.

4. Goldstein LJ, Gray R, Badve S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 2008;26:4063-71.

10. Klang SH, Hammerman A, Liebermann N, Efrat N, Doberne J, Hornberger J. Economic implications of 21-gene breast cancer risk assay from the perspective of an Israeli-managed health-care organization. Value Health 2010;13:381-7.

21. Lo SS, Mumby PB, Norton J, et al. Prospective multicenter study of the impact of the 21-gene recurrence score assay on medical oncologist and patient adjuvant breast cancer treatment selection. J Clin Oncol 2010;28:1671-6.

22. Asad J, Jacobson AF, Estabrook A, et al. Does Oncotype DX recurrence score affect the management of patients with early-stage breast cancer? Am J Surg 2008;196:527-9.

23. Erb C, Fox K, Patel M. Evaluation of practice patterns in the treatment of node-negative, hormone-receptor positive breast cancer patients with the use of the Oncotype DX assay at the University of Pennsylvania. Abstract #3082. In: 30th Annual San Antonio Breast Cancer Symposium. San Antonio, TX; 2007.

24. Habel LA, Shak S, Jacobs MK, et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res 2006;8:R25.

25. Liang H, Brufsky A, Lembersky B. A retrospective analysis of the impact of Oncotype DX low recurrence score results on treatment decisions in a single academic breast cancer center. Abstract #2061. In: 30th Annual San Antonio Breast Cancer Symposium. San Antonio TX; 2007.

26. Liebermann N, Baehner FL, Soussan-Gutman L, Klang S, Yoshizawa C, Shak S. Evaluation of Recurrence Score, nodal status and traditional clinicopathologic metrics in a large ER positive patient cohort. Abstract #1420. In: 2011 European Society for Medical Oncology. Providence, RI; 2011.

27. Oratz R, Kim B, Chao C, et al. Physician survey of the effect of the 21-gene recurrence score assay results on treatment recommendations for patients with lymph node-positive, estrogen receptor-positive breast cancer. J Oncol Pract 2011;7:94-9.

28. Oratz R, Paul D, Cohn AL, Sedlacek SM. Impact of a commercial reference laboratory test recurrence score on decision making in early-stage breast cancer. J Oncol Pract 2007;3:182-6.

29. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817-26.

30. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 2006;24:3726-34.

31. Partin JF, Mamounas EP. Impact of the 21-gene recurrence score assay compared with standard clinicopathologic guidelines in adjuvant therapy selection for node-negative, estrogen receptor-positive breast cancer. Ann Surg Oncol 2011;18:3399-406.

32. Thanasoulis T, Brown A, Frazier T. The role of Oncotype DX assay on appropriate treatment for estrogen positive, lymph node negative invasive breast cancer. In: American Society of Breast Surgeons Annual Meeting. New York, NY; 2008.