Biomarkers and surrogate end points—the challenge of statistical validation
The validation of predictive and prognostic biomarkers and surrogate end points requires robust statistical analysis of data gathered from multiple, large, independent studies. In this Review, Marc Buyse and coauthors discuss this validation process and the nature of biomarkers and surrogate end poi...
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| Published in: | Nature reviews. Clinical oncology Vol. 7; no. 6; pp. 309 - 317 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
01.06.2010
Nature Publishing Group |
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| ISSN: | 1759-4774, 1759-4782, 1759-4782 |
| Online Access: | Get full text |
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| Abstract | The validation of predictive and prognostic biomarkers and surrogate end points requires robust statistical analysis of data gathered from multiple, large, independent studies. In this Review, Marc Buyse and coauthors discuss this validation process and the nature of biomarkers and surrogate end points. Furthermore, they consider strategies for the pragmatic evaluation of biomarkers and surrogate end points in the absence of statistical validation.
Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation.
Key Points
Candidate prognostic biomarkers are relatively easy to identify, but multi-site validation has rarely been done
Predictive biomarkers require extensive data for validation, based on large randomized clinical trials and meta-analyses
Surrogate end points require data demonstrating both that the surrogate is prognostic of the true end point, and that the effect of treatment on the surrogate correlates with that of the true end point
The biological plausibility of a biomarker or surrogate might support its adoption even in cases where full statistical validation is lacking
No consensus exists on the best approach for pragmatic evaluation and adoption of biomarkers and surrogate end points when robust statistical validation is lacking |
|---|---|
| AbstractList | Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation.Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation. Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation. Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation. Buyse, M. et al. Nat. Rev. Clin. Oncol. 7, 309-317 (2010); published online 6 April 2010; doi: 10.1038/nrclinonc.2010.43 The validation of predictive and prognostic biomarkers and surrogate end points requires robust statistical analysis of data gathered from multiple, large, independent studies. In this Review, Marc Buyse and coauthors discuss this validation process and the nature of biomarkers and surrogate end points. Furthermore, they consider strategies for the pragmatic evaluation of biomarkers and surrogate end points in the absence of statistical validation. Biomarkers and surrogate end points have great potential for use in clinical oncology, but their statistical validation presents major challenges, and few biomarkers have been robustly confirmed. Provisional supportive data for prognostic biomarkers, which predict the likely outcome independently of treatment, is possible through small retrospective studies, but it has proved more difficult to achieve robust multi-site validation. Predictive biomarkers, which predict the likely response of patients to specific treatments, require more extensive data for validation, specifically large randomized clinical trials and meta-analysis. Surrogate end points are even more challenging to validate, and require data demonstrating both that the surrogate is prognostic for the true end point independently of treatment, and that the effect of treatment on the surrogate reliably predicts its effect on the true end point. In this Review, we discuss the nature of prognostic and predictive biomarkers and surrogate end points, and examine the statistical techniques and designs required for their validation. In cases where the statistical requirements for validation cannot be rigorously achieved, the biological plausibility of an end point or surrogate might support its adoption. No consensus yet exists on processes or standards for pragmatic evaluation and adoption of biomarkers and surrogate end points in the absence of robust statistical validation. Key Points Candidate prognostic biomarkers are relatively easy to identify, but multi-site validation has rarely been done Predictive biomarkers require extensive data for validation, based on large randomized clinical trials and meta-analyses Surrogate end points require data demonstrating both that the surrogate is prognostic of the true end point, and that the effect of treatment on the surrogate correlates with that of the true end point The biological plausibility of a biomarker or surrogate might support its adoption even in cases where full statistical validation is lacking No consensus exists on the best approach for pragmatic evaluation and adoption of biomarkers and surrogate end points when robust statistical validation is lacking |
| Audience | Academic |
| Author | Sargent, Daniel J. Matheson, Alastair Buyse, Marc de Gramont, Aimery Grothey, Axel |
| Author_xml | – sequence: 1 givenname: Marc surname: Buyse fullname: Buyse, Marc email: marc.buyse@iddi.com organization: International Drug Development Institute – sequence: 2 givenname: Daniel J. surname: Sargent fullname: Sargent, Daniel J. organization: Cancer Center Statistics, Mayo Clinic Cancer Center – sequence: 3 givenname: Axel surname: Grothey fullname: Grothey, Axel organization: Department of Medical Oncology, Mayo Clinic College of Medicine – sequence: 4 givenname: Alastair surname: Matheson fullname: Matheson, Alastair organization: Fondation ARCAD – sequence: 5 givenname: Aimery surname: de Gramont fullname: de Gramont, Aimery organization: Hôpital Saint-Antoine, Pavillon Moïana |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20368727$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Springer Nature Limited 2010 COPYRIGHT 2010 Nature Publishing Group Copyright Nature Publishing Group Jun 2010 |
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| Title | Biomarkers and surrogate end points—the challenge of statistical validation |
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