Biomarker Design: Lessons from Bayes' Theorem
In the last article I posted on “The Digital Biologist”, I gave a very brief and simple introduction to Bayes’ Theorem, using cancer biomarkers as an example of one of the many ways in which the theorem can be applied to the evaluation of data and evidence in life science R&D. The power of the Bayesian approach was I hope, evident in the analysis of the CA-125 biomarker for ovarian cancer that we considered, and I felt that it would be worthwhile in this follow-up, to round out our discussion by looking in a little more detail at the practical, actionable insights that can be gained by the application of Bayesian analysis to the design of biomarkers. It is all too often that those of us in the field of computational biology are accused of generating models, simulations and algorithms that while pretty or cool, are of little or no practical help to real world research problems. The sting of this accusation comes at least in part from the fact that all too often, this is actually true.