title: Constructing an ROC Curve to Assess a Treatment-Predictive Continuous Biomarker.
abstract: This paper presents the idea of an ROC curve, which quantifies the discriminatory potential of a continuous biomarker for treatment selection when the outcome is continuous. The analysis assumes data from a randomized parallel group design. We use non-parametric density estimators to construct an ROC curve based on the probabilities that a (non-)responder, defined by better (worse) response to treatment as opposed to control, in the treatment group has a biomarker value above a value c. Our non-parametric approach comes close to the true AUC in a simulation study based on a normal distribution. Application to a real data set shows that despite a significant interaction term in a proportional hazards model, a biomarker may not be helpful for treatment decisions. Our proof-of-principle study opens the door to further developments and generalizations.
authors: IBE, University of Munich.
authors: Alexander
authors: A
authors: Engelhardt
authors: IBE, University of Munich.
authors: Yu-Ming
authors: YM
authors: Shen
authors: IBE, University of Munich.
authors: Ulrich
authors: U
authors: Mansmann
mesh_terms: D019540:Area Under Curve; D015415:Biomarkers / Q000032:analysis*; D020000:Decision Support Systems, Clinical; D006801:Humans; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D018709:Statistics, Nonparametric
chemical_list: D015415:Biomarkers