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

Research output: Contribution to journalArticlepeer-review


  • 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

External organisations

  • University of Leeds
  • Department of Clinical Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire NHS Trust, Northwood, UK.
  • Cambridge University Hospitals NHS Foundation Trust
  • University of Warwick
  • Ontario Institute for Cancer Research
  • University of Edinburgh
  • University of Glasgow
  • National Cancer Research Institute Consumer Liaison Group (NCRI CLG); Independent Cancer Patients Voice (ICPV); London UK
  • University College London Hospitals NHS Foundation Trust
  • Grant MacEwan University, Edmonton, Alberta, Canada


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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