This article is published in Pharmaceutical Statistics as a special issue on Focusing on the PSI Special Interest Groups, edited by John Stevens, Centre for Bayesian Statistics in Health Economics, ScHARR, Regent Court, 30 Regent Street, Sheffield, South Yorkshire, S1 4DA, UK.
A statistician's perspective on biomarkers in drug development
Version of Record online: 8 DEC 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Special Issue: Focusing on the PSI Special Interest Groups
Volume 10, Issue 6, pages 494–507, November/December 2011
How to Cite
Jenkins, M., Flynn, A., Smart, T., Harbron, C., Sabin, T., Ratnayake, J., Delmar, P., Herath, A., Jarvis, P., Matcham, J. and on behalf of the PSI Biomarker Special Interest Group (2011), A statistician's perspective on biomarkers in drug development. Pharmaceut. Statist., 10: 494–507. doi: 10.1002/pst.532
- Issue online: 28 DEC 2011
- Version of Record online: 8 DEC 2011
- personalized health care;
- safety biomarkers;
- surrogate endpoint
Biomarkers play an increasingly important role in many aspects of pharmaceutical discovery and development, including personalized medicine and the assessment of safety data, with heavy reliance being placed on their delivery. Statisticians have a fundamental role to play in ensuring that biomarkers and the data they generate are used appropriately and to address relevant objectives such as the estimation of biological effects or the forecast of outcomes so that claims of predictivity or surrogacy are only made based upon sound scientific arguments. This includes ensuring that studies are designed to answer specific and pertinent questions, that the analyses performed account for all levels and sources of variability and that the conclusions drawn are robust in the presence of multiplicity and confounding factors, especially as many biomarkers are multidimensional or may be an indirect measure of the clinical outcome. In all of these areas, as in any area of drug development, statistical best practice incorporating both scientific rigor and a practical understanding of the situation should be followed. This article is intended as an introduction for statisticians embarking upon biomarker-based work and discusses these issues from a practising statistician's perspective with reference to examples. Copyright © 2011 John Wiley & Sons, Ltd.