Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers

Daniel J. Sargent, Sumithra J. Andrekar

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Background Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. Purpose The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. Results A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Conclusions Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward. Clinical Trials 2013; 10: 647-652. http://ctj.sagepub.com.

Original languageEnglish (US)
Pages (from-to)647-652
Number of pages6
JournalClinical Trials
Volume10
Issue number5
DOIs
StatePublished - Oct 2013

Fingerprint

Biomarkers
Random Allocation
Validation Studies
Research
Randomized Controlled Trials
Clinical Trials

ASJC Scopus subject areas

  • Medicine(all)
  • Pharmacology

Cite this

Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers. / Sargent, Daniel J.; Andrekar, Sumithra J.

In: Clinical Trials, Vol. 10, No. 5, 10.2013, p. 647-652.

Research output: Contribution to journalArticle

Sargent, Daniel J. ; Andrekar, Sumithra J. / Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers. In: Clinical Trials. 2013 ; Vol. 10, No. 5. pp. 647-652.
@article{443aea9275fc443cba603e7923a633bc,
title = "Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers",
abstract = "Background Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. Purpose The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. Results A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Conclusions Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward. Clinical Trials 2013; 10: 647-652. http://ctj.sagepub.com.",
author = "Sargent, {Daniel J.} and Andrekar, {Sumithra J.}",
year = "2013",
month = "10",
doi = "10.1177/1740774513497125",
language = "English (US)",
volume = "10",
pages = "647--652",
journal = "Clinical Trials",
issn = "1740-7745",
publisher = "SAGE Publications Ltd",
number = "5",

}

TY - JOUR

T1 - Statistical issues in the validation of prognostic, predictive, and surrogate biomarkers

AU - Sargent, Daniel J.

AU - Andrekar, Sumithra J.

PY - 2013/10

Y1 - 2013/10

N2 - Background Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. Purpose The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. Results A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Conclusions Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward. Clinical Trials 2013; 10: 647-652. http://ctj.sagepub.com.

AB - Background Biomarkers have many distinct purposes, and depending on their intended use, the validation process varies substantially. Purpose The goal of this article is to provide an introduction to the topic of biomarkers, and then to discuss three specific types of biomarkers, namely, prognostic, predictive, and surrogate. Results A principle challenge for biomarker validation from a statistical perspective is the issue of multiplicity. In general, the solution to this multiplicity challenge is well known to statisticians: pre-specification and replication. Critical requirements for prognostic marker validation include uniform treatment, complete follow-up, unbiased case selection, and complete ascertainment of the many possible confounders that exist in the context of an observational sample. In the case of predictive biomarker validation, observational data are clearly inadequate and randomized controlled trials are mandatory. Within the context of randomization, strategies for predictive marker validation can be grouped into two categories: retrospective versus prospective validation. The critical validation criteria for a surrogate endpoint is to ensure that if a trial uses a surrogate endpoint, the trial will result in the same inferences as if the trial had observed the true endpoint. The field of surrogate endpoint validation has now moved to the multi-trial or meta-analytic setting as the preferred method. Conclusions Biomarkers are a highly active research area. For all biomarker developmental and validation studies, the importance of fundamental statistical concepts remains the following: pre-specification of hypotheses, randomization, and replication. Further statistical methodology research in this area is clearly needed as we move forward. Clinical Trials 2013; 10: 647-652. http://ctj.sagepub.com.

UR - http://www.scopus.com/inward/record.url?scp=84886941454&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886941454&partnerID=8YFLogxK

U2 - 10.1177/1740774513497125

DO - 10.1177/1740774513497125

M3 - Article

C2 - 23983158

AN - SCOPUS:84886941454

VL - 10

SP - 647

EP - 652

JO - Clinical Trials

JF - Clinical Trials

SN - 1740-7745

IS - 5

ER -