Nonparametric Generalized Fiducial Inference for Survival Functions Under Censoring

• Activity Number: 234 - SBSS Student Travel Award Session 2
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yifan Cui* and Jan Hannig
• Companies: University of North Carolina at Chapel Hill and University of North Carolina
• Keywords: Generalized fiducial inference; Right censored data; Nonparametric model; Coverage; Testing

Abstract

In this paper, we present what might be the first time fiducial inference, as generalized by Hannig et al. (2016), is systematically applied to estimation of a nonparametric survival function under right censoring. The resulting fiducial distribution gives rise to surprisingly good statistical procedures. In particular, we use the fiducial distribution of a survival function to construct pointwise and curvewise confidence intervals for the survival function, and propose tests based on the curvewise confidence interval. We establish a functional Bernstein-von Mises theorem. The proposed fiducial based confidence intervals maintain coverage in situations where asymptotic methods often have substantial coverage problems. Furthermore, the average length of the proposed confidence intervals is often shorter than the length of competing methods that maintain coverage. Finally, the proposed fiducial test is more powerful than various types of log-rank tests and sup log-rank tests in some scenarios. We illustrate the proposed fiducial test comparing chemotherapy against chemotherapy combined with radiotherapy using data from the treatment of locally unresectable gastric cancer.