Use of auxiliary covariates in estimating a biomarker-adjusted treatment effect model with clinical trial data. (October 2016)

• Record Type: Journal Article
• Authors: Zhang, Zhiwei; Qu, Yanping; Zhang, Bo; Nie, Lei; Soon, Guoxing
• Is Part Of: Statistical methods in medical research. Volume 25:Number 5(2016:Oct.)
• Page Start: 2103
• Page End: 2119
• Publication Date: 2016-10
• Subjects: conditional effect -- interaction -- personalized medicine -- predictive biomarker -- treatment effect heterogeneity -- treatment selection; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280213515572
• Languages: English
• ISSNs: 0962-2802

Abstract

A biomarker-adjusted treatment effect (BATE) model describes the effect of one treatment versus another on a subpopulation of patients defined by a biomarker. Such a model can be estimated from clinical trial data without relying on additional modeling assumptions, and the estimator can be made more efficient by incorporating information on the main effect of the biomarker on the outcome of interest. Motivated by an HIV trial known as THRIVE, we consider the use of auxiliary covariates, which are usually available in clinical trials and have been used in overall treatment comparisons, in estimating a BATE model. Such covariates can be incorporated using an existing augmentation technique. For a specific type of estimating functions for difference-based BATE models, the optimal augmentation depends only on the joint main effects of marker and covariates. For a ratio-based BATE model, this result holds in special cases but not in general; however, simulation results suggest that the augmentation based on the joint main effects of marker and covariates is virtually equivalent to the theoretically optimal augmentation, especially when the augmentation terms are estimated from data. Application of these methods and results to the THRIVE data yields new insights on the utility of baseline CD4 cell count and viral load as predictive or treatment selection markers.