The Use of Covariates and Random Effects in Evaluating Predictive Biomarkers Under a Potential Outcome Framework.

ABSTRACT: Predictive or treatment selection biomarkers are usually evaluated in a subgroup or regression analysis with focus on the treatment-by-marker interaction. Under a potential outcome framework (Huang, Gilbert and Janes [Biometrics68 (2012) 687-696]), a predictive biomarker is considered a predictor for a desirable treatment benefit (defined by comparing potential outcomes for different treatments) and evaluated using familiar concepts in prediction and classification. However, the desired treatment benefit is un-observable because each patient can receive only one treatment in a typical study. Huang et al. overcome this problem by assuming monotonicity of potential outcomes, with one treatment dominating the other in all patients. Motivated by an HIV example that appears to vio

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC4714717 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature