domingo, 20 de abril de 2014

Cost Effectiveness of a 92-Gene Assay for the Dia... [J Med Econ. 2014] - PubMed - NCBI

Cost Effectiveness of a 92-Gene Assay for the Dia... [J Med Econ. 2014] - PubMed - NCBI

 2014 Apr 1. [Epub ahead of print]

Cost Effectiveness of a 92-Gene Assay for the Diagnosis of Metastatic Cancer.


Abstract OBJECTIVES: To estimate the clinical and economic trade-offs involved in using a molecular assay (92-gene assay, CancerTYPE ID®) to aid in identifying the primary site of difficult-to-diagnose metastatic cancers and to explore whether the 92-gene assay can be used to standardize the diagnostic process and costs for clinicians, patients, and payers.


Four decision-analytic models were developed to project the lifetime clinical and economic impact of incorporating the 92-gene assay compared with standard care alone. For each model, total and incremental costs, life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and the proportion of patients treated correctly versus incorrectly were projected from the payer perspective. Model inputs were based on published literature, analyses of SEER (Surveillance Epidemiology and End Results) data, publicly available data, and interviews with clinical experts.


In all four models, the 92-gene assay increased the proportion of patients treated correctly, decreased the proportion of patients treated with empiric therapy, and increased quality-adjusted survival. In the primary model, the ICER was $50,273/QALY; thus, the 92-gene assay is therefore cost effective when considering a societal willingness-to-pay threshold of $100,000/QALY. These findings were robust across sensitivity analyses.


Use of the 92-gene assay for diagnosing metastatic tumors of uncertain origin is associated with reduced misdiagnoses, increased survival, and improved quality of life. Incorporating the assay into current practice is a cost-effective approach to standardizing diagnostic methods while improving patient care. Limitations of this analysis are the lack of data availability and resulting modeling simplifications, although sensitivity analyses showed these to not be key drivers of results.

[PubMed - as supplied by publisher]

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