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Adaptive index models for marker-based risk stratification.

ABSTRACT: 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.

SUBMITTER: Tian L

PROVIDER: S-EPMC3006126 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Adaptive index models for marker-based risk stratification.

Tian Lu L Tibshirani Robert R

Biostatistics (Oxford, England) 20100727 1

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 mode ...[more]

PMID: 20663850

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