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. 2013 Feb 5:346:e5793.
doi: 10.1136/bmj.e5793.

Prognosis research strategy (PROGRESS) 4: stratified medicine research

Aroon D Hingorani  1 , Daniëlle A van der Windt, Richard D Riley, Keith Abrams, Karel G M Moons, Ewout W Steyerberg, Sara Schroter, Willi Sauerbrei, Douglas G Altman, Harry Hemingway; PROGRESS Group
Collaborators, Affiliations

Prognosis research strategy (PROGRESS) 4: stratified medicine research

Aroon D Hingorani et al. BMJ. .

Abstract

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

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work. SS is a full time employee of the BMJ Group but is not involved in deciding which manuscripts are accepted for publication.

Figures

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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 cancer to development of trastuzumab treatment and use in clinical practice. Path element adapted from chart 7.1 in the Cooksey report (2006) (made available for use through the Open Government License)
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Fig 2 Estimated treatment effect in subgroups defined according to (upper panel) risk from a prognostic model and (lower panel) a factor that predicts differential treatment response. The prevalence of positive factor and high risk is shown, arbitrarily, as 20%. The dotted vertical line shows the overall treatment effect, the centre of each box shows the effect estimate, and the horizontal lines show confidence intervals
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Fig 3 Example of spurious finding in meta-analysis of summary data refuted by meta-analysis of individual participant data: whether antihypertensive treatment has a greater effect in women than men (reproduced with permission from Riley et al46 53)
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Fig 4 Commonly used (but suboptimal) study designs in assessment of a factor that potentially predicts differential treatment response
See this image and copyright information in PMC

References

    1. Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ 2013;346:e5595. - PMC - PubMed
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    1. Wolff AC, Hammond ME, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2007;25:118-45. - PubMed
    1. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 1987;235:177-82. - PubMed

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