Adaptive index models for marker-based risk stratification

We use the term 'index predictor' to denote a score that consists of K binary rules such as 'age > 60' or 'blood pressure > 120 mm Hg'. The index predictor is the sum of these binary scores, yielding a value from 0 to K. Such indices as often used in clinical studies to stratify population risk: They are usually derived from subject area considerations.In this paper, we propose a fast data-driven procedure for automatically constructing such indices for linear, logistic, and Cox regression models.We also extend the procedure to create indices for detecting treatment.marker interactions.The methods are illustrated on a study with protein biomarkers as well as a large microarray gene expression study.