title: Inference on treatment-covariate interaction based on a nonparametric measure of treatment effects and censored survival data.
abstract: The investigation of the treatment-covariate interaction is of considerable interest in the design and analysis of clinical trials. With potentially censored data observed, non-parametric and semi-parametric estimates and associated confidence intervals are proposed in this paper to quantify the interactions between the treatment and a binary covariate. In addition, comparison of interactions between the treatment and two covariates are also considered. The proposed approaches are evaluated and compared by Monte Carlo simulations and applied to a real data set from a cancer clinical trial. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada.
authors.forename: Shan
authors.initials: S
authors.lastname: Jiang
authors.affiliation: Canadian Cancer Trials Group, Queen's University, Kingston, Ontario, Canada.
authors.forename: Bingshu
authors.initials: B
authors.lastname: Chen
authors.affiliation: Canadian Cancer Trials Group, Queen's University, Kingston, Ontario, Canada.
authors.forename: Dongshengn
authors.initials: D
authors.lastname: Tu
mesh_terms: D006801:Humans; D009010:Monte Carlo Method; D016019:Survival Analysis
keywords: biomarker; clinical trial; confidence interval; density ratio model; empirical likelihood; interaction; non-parametric inference