Economics of Genomic Testing for Women With Breast Cancer | Page 3

Published Online: December 23, 2013
Robert D. Lieberthal, PhD
One study included in our comparison discussed cost-effectiveness analyses of a 70-gene expression-profiling test (MammaPrint). This study, performed in 2005, found that MammaPrint had lower costs and worse outcomes, in terms of lower life expectancy and QALYs, than the National Institutes of Health clinical guidelines for treatment of breast cancer. Specifically, the study found that use of the test “resulted in an absolute 5% decrease in the proportion of cases of distant recurrence prevented, 0.21 fewer QALYs, and a cost savings of $2882.”15 A more recent study performed in 2010, also in our comparison, found that MammaPrint had a favorable  incremental cost-effectiveness ratio of $7000 per QALY and $10,000 per life-year saved compared with Adjuvant! Online software  guidance.7 The contradictory findings from these 2 studies are primarily due to the control group being used—National Institutes of  Health guidelines in one case and decision software in the other case. The findings may also be related to the timing of these  studies, one of which was published 5 years later than the other.

Two international studies included in our comparison conducted generic cost-effectiveness analyses of genomic testing rather than selecting a specific marketed product. One European study used a different type of genomic test (189-gene expression profiling) not usually available in the US market and applied cost-minimization analysis.28 They found that compared with conventional adjuvant chemotherapy, genomic testing is cost-effective only if the test cost is less than €2090 ($2720).28 A Brazilian study focused on the financial impact of using genomic testing (21-gene expression profiling) by taking into account the medical costs and excluding  utility or QALY measures.19 Bacchi and colleagues19 conducted a Web-based survey among medical oncologists in Brazil and  compared the costs associated with individual decisions for treating hypothetical patients with the costs associated with 21-gene  expression assay–guided decisions. They found that for a hypothetical cohort of 100 patients with access to the test, $79,400  would be saved in direct medical costs.

Perspectives Represented in Our Review

In our economic analyses we compared, payer, provider, and societal perspectives. A total of 3 studies examined the societal perspective, 4 described the payer perspective, and 2 studies used a provider perspective (Table 1).

Those studies that used the societal perspective included relevant costs and outcomes that were associated with breast cancer in their models, as well as cost utility. Including cost utility proved extremely useful.

Studies using the payer perspective compared the actual cost per QALY with the acceptable threshold in order to determine the cost-effectiveness of the assay.21 Out-of-pocket patient costs and indirect medical costs are not included in the payer perspective.


Data Versus Modeling Methods

The studies reviewed are largely based on data from clinical trials. They all rely on economic modeling methods to generate cost predictions or projections. A variety of modeling methods were used, most commonly Markov modeling. Cost-effectiveness analysis and budget impact analysis were also used.


These economic studies took into account the opportunistic cost of having unnecessary chemotherapy for a certain subpopulation and humanistic measures such as quality of life. The majority of studies provided monetary thresholds for each product in the market, which helped enable economic decision making about whether to use genomic testing.


Economic modeling has significant limitations compared with clinical trials. Because these studies are usually extrapolated from  clinical trial data, the same prognostic accuracy may not apply in a real-world setting. The Evaluation of Genomic Applications in Practice and Prevention Working Group identified 2 additional concerns. One is that details and characteristics about certain tests  are not published. Additionally, “concerns about the parameter estimates, lack of sensitivity analyses to assess sources of bias,  and changes in the National Comprehensive Cancer Network (NCCN) guidelines reduce the confidence and relevance of one of  these studies.”2 We agree that comparative effectiveness analysis in this area is challenging because the outcome of value is more  difficult to measure than clinical end points and because technology is evolving so rapidly. To improve the economic evidence base for these tests, we believe that there should be increased funding for economic evaluation and that the results of any testing should be made more transparent.

Future Research and the Case for the Economic Benefit of Genomic Testing

There is a great need for additional translational research, including both clinical and outcomes research.29,30 There is also an opportunity to develop tests that can inform treatment  decisions for subpopulations for whom current tests are not applicable, such  as patients diagnosed with triple-negative breast cancer or those with ER-negative status. There may also be interest in comparative effectiveness research results from head-to-head trials comparing genomic tests and treatment outcomes.

Genomic medicine requires additional evidence about the value of these tests to reach its full potential.31 A value calculation requires evidence of both clinical utility and economic effectiveness.32 Several articles discuss the barriers genomic medicine faces  with regard to making the case for value. Experts recommend additional funding for trials oftranslational research, specifically in  phases 2 through 4, additional evidence of clinical utility,33 more outcomes data, and improved regulation of genomic test  production.34,35



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Issue: December 2013
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