The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment

Holly Janes, Margaret S. Pepe, Lisa M. McShane, Daniel J. Sargent, Patrick J. Heagerty

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.

Original languageEnglish (US)
Article numberdjv157
JournalJournal of the National Cancer Institute
Volume107
Issue number8
DOIs
StatePublished - Aug 1 2015

Fingerprint

Biomarkers
Standard of Care
Therapeutics
Aptitude
Language
Medicine
Sensitivity and Specificity
Population

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment. / Janes, Holly; Pepe, Margaret S.; McShane, Lisa M.; Sargent, Daniel J.; Heagerty, Patrick J.

In: Journal of the National Cancer Institute, Vol. 107, No. 8, djv157, 01.08.2015.

Research output: Contribution to journalArticle

Janes, Holly ; Pepe, Margaret S. ; McShane, Lisa M. ; Sargent, Daniel J. ; Heagerty, Patrick J. / The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment. In: Journal of the National Cancer Institute. 2015 ; Vol. 107, No. 8.
@article{d660ec692aa44582a0c48be4b41ba242,
title = "The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment",
abstract = "Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.",
author = "Holly Janes and Pepe, {Margaret S.} and McShane, {Lisa M.} and Sargent, {Daniel J.} and Heagerty, {Patrick J.}",
year = "2015",
month = "8",
day = "1",
doi = "10.1093/jnci/djv157",
language = "English (US)",
volume = "107",
journal = "Journal of the National Cancer Institute",
issn = "0027-8874",
publisher = "Oxford University Press",
number = "8",

}

TY - JOUR

T1 - The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment

AU - Janes, Holly

AU - Pepe, Margaret S.

AU - McShane, Lisa M.

AU - Sargent, Daniel J.

AU - Heagerty, Patrick J.

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.

AB - Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.

UR - http://www.scopus.com/inward/record.url?scp=84939602784&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939602784&partnerID=8YFLogxK

U2 - 10.1093/jnci/djv157

DO - 10.1093/jnci/djv157

M3 - Article

C2 - 26109106

AN - SCOPUS:84939602784

VL - 107

JO - Journal of the National Cancer Institute

JF - Journal of the National Cancer Institute

SN - 0027-8874

IS - 8

M1 - djv157

ER -