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Research Methods & Reporting

Prognosis research strategy (PROGRESS) 4: Stratified medicine research

BMJ 2013; 346 doi: (Published 05 February 2013) Cite this as: BMJ 2013;346:e5793
  1. Aroon D Hingorani, professor of genetic epidemiology1,
  2. Daniëlle A van der Windt, professor in primary care epidemiology2,
  3. Richard D Riley, senior lecturer in medical statistics3,
  4. Keith Abrams, professor of medical statistics4,
  5. Karel G M Moons, professor of clinical epidemiology5,
  6. Ewout W Steyerberg, professor of medical decision making6,
  7. Sara Schroter, senior researcher7,
  8. Willi Sauerbrei, professor of medical biometry8,
  9. Douglas G Altman, professor of statistics in medicine9,
  10. Harry Hemingway, professor of clinical epidemiology1
  11. for the PROGRESS Group
  1. 1Department of Epidemiology and Public Health, University College London, London WC1E 7HB, UK
  2. 2Arthritis Research UK Primary Care Centre, Primary Care Sciences, Keele University, Keele ST5 5BG, UK
  3. 3School of Health and Population Sciences, University of Birmingham, Birmingham B15 2TT, UK
  4. 4Centre for Biostatistics & Genetic Epidemiology, Department of Health Sciences, School of Medicine, University of Leicester, Leicester LE1 7RH, UK
  5. 5Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands
  6. 6Department of Public Health, Erasmus MC, 3000 CA Rotterdam, Rotterdam, Netherlands
  7. 7BMJ, BMA House, London WC1H 9JR, UK
  8. 8Institute of Medical Biometry and Informatics, University Medical Center Freiburg, 79104 Freiburg, Germany
  9. 9Centre for Statistics in Medicine, University of Oxford, Oxford OX2 6UD, UK
  1. Correspondence to: H Hemingway h.hemingway{at}
  • Accepted 29 July 2012

In patients with a particular disease or health condition, stratified medicine seeks to identify those who will have the most clinical benefit or least harm from a specific treatment. In this article, the fourth in the PROGRESS series, the authors discuss why prognosis research should form a cornerstone of stratified medicine, especially in regard to the identification of factors that predict individual treatment response

A woman with newly diagnosed breast cancer is deciding on a course of therapy, guided by her physician. Evidence on the average prognosis1 and effectiveness of therapeutic interventions is available from studies of large groups of patients with breast cancer in observational studies and randomised trials. But the patient and doctor are faced with making a decision in an individual case, where the prognosis and response to treatment may deviate from average. One way to select the optimal treatment is to consider a test that predicts treatment effect, such as the human epidermal growth factor receptor 2 (HER-2) status.2 The use of HER-2 status in breast cancer management is an example of the translation of results from prognosis research toward improved patient outcomes. The prognosis of breast cancer patients is highly variable,1 HER-2 was discovered as a prognostic factor,3 which provided a specific target for an intervention (trastuzumab), which was then evaluated in trials which recruited women with HER-2 positive cancers (see fig 1). After the success of these trials in improving clinical outcome, trastuzumab is now given to the subgroup (stratum) of women who are HER-2 positive, but not to those testing negative;4 this type of approach has been termed stratified medicine.

Fig 1 Example of stratified medicines research, with translation from discovery of human epidermal growth factor receptor 2 (HER-2) status as a prognostic factor for metastatic breast …

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