Open Access
December 2014 The use of covariates and random effects in evaluating predictive biomarkers under a potential outcome framework
Zhiwei Zhang, Lei Nie, Guoxing Soon, Aiyi Liu
Ann. Appl. Stat. 8(4): 2336-2355 (December 2014). DOI: 10.1214/14-AOAS773


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 [Biometrics 68 (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 unobservable 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.


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Zhiwei Zhang. Lei Nie. Guoxing Soon. Aiyi Liu. "The use of covariates and random effects in evaluating predictive biomarkers under a potential outcome framework." Ann. Appl. Stat. 8 (4) 2336 - 2355, December 2014.


Published: December 2014
First available in Project Euclid: 19 December 2014

zbMATH: 06408781
MathSciNet: MR3292500
Digital Object Identifier: 10.1214/14-AOAS773

Keywords: Conditional independence , counterfactual , ROC regression , sensitivity analysis , treatment effect heterogeneity , treatment selection

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 4 • December 2014
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