title: Use of auxiliary covariates in estimating a biomarker-adjusted treatment effect model with clinical trial data.
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.
authors.affiliation: Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA Zhiwei.Zhang@fda.hhs.gov.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Qu
authors.affiliation: Biostatistics Core, School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Division of Biometrics IV, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
authors.affiliation: Division of Biometrics IV, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Guoxing
authors.initials: G
authors.lastname: Soon
mesh_terms: D054316:Biomarkers, Pharmacological; D018791:CD4 Lymphocyte Count; D002986:Clinical Trials as Topic; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D057285:Precision Medicine; D018709:Statistics, Nonparametric; D019562:Viral Load
chemical_list: D054316:Biomarkers, Pharmacological
keywords: conditional effect; interaction; personalized medicine; predictive biomarker; treatment effect heterogeneity; treatment selection