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Adv Exp Med Biol. 2015;867:81-90. doi: 10.1007/978-94-017-7215-0_6.

Efficient, Adaptive Clinical Validation of Predictive Biomarkers in Cancer Therapeutic Development.

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Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC, 2007, USA.
, 1551 33rd Street NW, Washington, DC, 2007, USA.
Biostatistics and Research Decision Sciences, Merck Research Laboratories, Rahway, NJ, USA.


Predictive biomarkers, defined as biomarkers that can be used to identify patient populations who will optimally benefit from therapy, are an important part of the future of oncology. They have the potential to reduce the size and cost of clinical development programs for oncology therapy, while increasing their probability of success and the ultimate value of cancer medicines. But predictive biomarkers do not always work, and under these circumstances they add cost, complexity, and time to drug development. This chapter describes Phase 2 and 3 development methods which efficiently and adaptively evaluate the ability of the biomarker to predict clinical outcomes. In the end, the biomarker is emphasized to the extent that it is actually predictive. This allows clinical cancer drug developers to manage uncertainty in the validity of biomarkers, leading to maximal value for predictive biomarkers and their associated oncology therapies.


Clinical outcome; Clinical performance; Clinical practice; Clinical validation; Linearity; Oncology personalized medicine; Oncology therapies; Phase 1 clinical studies; Phase 2 clinical studies; Phase 3 clinical studies; Predictive biomarker; Reproducibility; Sensitivity; Specificity; Stratification; The multiple comparisons problem; Translational medicine

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