Biomarkers and surrogate end points--the challenge of statistical validation

Abstract

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.