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Inference on treatment‐covariate interaction based on a nonparametric measure of treatment effects and censored survival data
Author(s) -
Jiang Shan,
Chen Bingshu,
Tu Dongshengn
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6907
Subject(s) - covariate , nonparametric statistics , statistics , inference , parametric statistics , econometrics , data set , confidence interval , survival analysis , estimator , computer science , mathematics , artificial intelligence
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.