Value of information analysis of multiparameter tests for chemotherapy in early breast cancer: the OPTIMA prelim trial

Peter S. Hall, Alison Smith, Claire Hulme, Armando Vargas-Palacios, Andreas Makris, Luke Hughes-Davies, Janet A Dunn, John M S Bartlett, David A. Cameron, Andrea Marshall, Amy Campbell, Iain R. Macpherson, Daniel Rea, Adele Francis, Helena Earl, Adrienne Morgan, Robert C. Stein, Christopher McCabe, OPTIMA Trial Management Group

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
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BACKGROUND: Precision medicine is heralded as offering more effective treatments to smaller targeted patient populations. In breast cancer, adjuvant chemotherapy is standard for patients considered as high-risk after surgery. Molecular tests may identify patients who can safely avoid chemotherapy.

OBJECTIVES: To use economic analysis before a large-scale clinical trial of molecular testing to confirm the value of the trial and help prioritize between candidate tests as randomized comparators.

METHODS: Women with surgically treated breast cancer (estrogen receptor-positive and lymph node-positive or tumor size ≥30 mm) were randomized to standard care (chemotherapy for all) or test-directed care using Oncotype DX™. Additional testing was undertaken using alternative tests: MammaPrintTM, PAM-50 (ProsignaTM), MammaTyperTM, IHC4, and IHC4-AQUA™ (NexCourse Breast™). A probabilistic decision model assessed the cost-effectiveness of all tests from a UK perspective. Value of information analysis determined the most efficient publicly funded ongoing trial design in the United Kingdom.

RESULTS: There was an 86% probability of molecular testing being cost-effective, with most tests producing cost savings (range -£1892 to £195) and quality-adjusted life-year gains (range 0.17-0.20). There were only small differences in costs and quality-adjusted life-years between tests. Uncertainty was driven by long-term outcomes. Value of information demonstrated value of further research into all tests, with Prosigna currently being the highest priority for further research.

CONCLUSIONS: Molecular tests are likely to be cost-effective, but an optimal test is yet to be identified. Health economics modeling to inform the design of a randomized controlled trial looking at diagnostic technology has been demonstrated to be feasible as a method for improving research efficiency.

Original languageEnglish
Pages (from-to)1311-1318
Number of pages8
JournalValue in Health
Issue number10
Early online date11 Jul 2017
Publication statusPublished - Dec 2017


  • Journal Article
  • breast cancer
  • efficient research design
  • personalized medicine
  • value of information analysis


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