Modern medicine has graduated from broad spectrum treatments to molecularly targeted therapeutics. A major medical challenge is to develop companion diagnostics that provide guidance in directing use of these drugs to patients whose prognosis warrants them and whose disease is likely to be responsive to them. With continued advances in high throughput biotechnology, it is feasible to run whole genome assays on disease tissue whose features can be developed into prognostic and predictive biomarkers.
Specialized statistical methods are required for the development and evaluation of multivariate biomarkers using such assays because the number of candidate features is generally much greater than the number of patients. The goal of this module is to introduce ideas in high dimensional predictive modeling, to discuss model validation and testing, and to give hands-on experience with these tools to build predictive biomarkers on real, high-dimensional datasets.
Through the course, participants will become familiar with various methods in penalized regression, applications of cross-validation, as well as ideas in multiplicity and selection bias. Participants will gain experience applying these methods in R.