title: 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 violate the monotonicity assumption, we propose a different approach based on covariates and random effects for evaluating predictive biomarkers under the potential outcome framework. Under the proposed approach, the parameters of interest can be identified by assuming conditional independence of potential outcomes given observed covariates, and a sensitivity analysis can be performed by incorporating an unobserved random effect that accounts for any residual dependence. Application of this approach to the motivating example shows that baseline viral load and CD4 cell count are both useful as predictive biomarkers for choosing antiretroviral drugs for treatment-naive patients.
authors.affiliation: Center for Devices and Radiological Health, Food and Drug Administration.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Center for Drug Evaluation and Research, Food and Drug Administration.
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
authors.affiliation: Center for Drug Evaluation and Research, Food and Drug Administration.
authors.forename: Guoxing
authors.initials: G
authors.lastname: Soon
authors.affiliation: Eunice Kennedy Shriver National Institute of Child Health and Human Development.
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
keywords: Conditional independence; ROC regression; counterfactual; sensitivity analysis; treatment effect heterogeneity; treatment selection