The use of covariates and random effects in evaluating predictive biomarkers under a potential outcome framework

TL;DR

This paper introduces a new method using covariates and random effects to evaluate predictive biomarkers within a potential outcome framework, addressing limitations of previous assumptions and enabling more flexible analysis.

Contribution

It proposes a covariate and random effects-based approach for evaluating predictive biomarkers, relaxing the monotonicity assumption and allowing for residual dependence in potential outcomes.

Findings

Baseline viral load is a useful predictive biomarker.

CD4 cell count is a useful predictive biomarker.

The method provides a flexible framework for biomarker evaluation.

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 [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.