title: Simultaneous confidence bands for Cox regression from semiparametric random censorship.
abstract: Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.
authors.affiliation: Center for Applied Mathematics and Statistics, Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ, USA.
authors.forename: Shoubhik
authors.initials: S
authors.lastname: Mondal
authors.affiliation: Center for Applied Mathematics and Statistics, Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ, USA. sundars@njit.edu.
authors.forename: Sundarraman
authors.initials: S
authors.lastname: Subramanian
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D016030:Kidney Transplantation / Q000401:mortality; D016013:Likelihood Functions; D008105:Liver Cirrhosis, Biliary / Q000401:mortality; D016016:Proportional Hazards Models; D016019:Survival Analysis
keywords: Counting process; Empirical coverage; Equal-precision; Gaussian multiplier bootstrap; Martingale; Strong consistency