Cost Effectiveness of Gene Expression Profile Testing in Community Practice.

Chandler Y1, Schechter CB1, Jayasekera J1, Near A1, O'Neill SC1, Isaacs C1, Phelps CE1, Ray GT1, Lieu TA1, Ramsey S1, Mandelblatt JS1.

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Abstract

Purpose Gene expression profile (GEP) testing can support chemotherapy decision making for patients with early-stage, estrogen receptor-positive, human epidermal growth factor 2-negative breast cancers. This study evaluated the cost effectiveness of one GEP test, Onco type DX (Genomic Health, Redwood City, CA), in community practice with test-eligible patients age 40 to 79 years. Methods A simulation model compared 25-year societal incremental costs and quality-adjusted life-years (QALYs) of community Onco type DX use from 2005 to 2012 versus usual care in the pretesting era (2000 to 2004). Inputs included Onco type DX and chemotherapy data from an integrated health care system and national and published data on Onco type DX accuracy, chemotherapy effectiveness, utilities, survival and recurrence, and Medicare and patient costs. Sensitivity analyses varied individual parameters; results were also estimated for ideal conditions (ie, 100% testing and adherence to test-suggested treatment, perfect test accuracy, considering test effects on reassurance or worry, and lowest costs). Results Twenty-four percent of test-eligible patients had Onco type DX testing. Testing was higher in younger patients and patients with stage I disease ( v stage IIA), and 75.3% and 10.2% of patients with high and low recurrence risk scores received chemotherapy, respectively. The cost-effectiveness ratio for testing ( v usual care) was $188,125 per QALY. Considering test effects on worry versus reassurance decreased the cost-effectiveness ratio to $58,431 per QALY. With perfect test accuracy, the cost-effectiveness ratio was $28,947 per QALY, and under ideal conditions, it was $39,496 per QALY. Conclusion GEP testing is likely to have a high cost-effectiveness ratio on the basis of community practice patterns. However, realistic variations in assumptions about key variables could result in GEP testing having cost-effectiveness ratios in the range of other accepted interventions. The differences in cost-effectiveness ratios on the basis of community versus ideal conditions underscore the importance of considering real-world implementation when assessing the new technology.