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.
Tian
Lu
L
Department of Health Research & Policy, Stanford University, Stanford, CA 94305, USA. lutian@stanford.edu
Tibshirani
Robert
R
D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000175:diagnosis; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods*
D015415:Biomarkers