A Unified Nonparametric Fiducial Approach to Interval-Censored Data

• 成果类型: Article
• 署名作者: Cui, Yifan; Hannig, Jan; Kosorok, Michael R.
• 署名单位: Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2252143
• 发表日期: 2024
• 页码: 2230-2241
• 关键词: failure time model; asymptotic properties; maximum-likelihood; inference; algorithm; Consistency; estimator; parameter; bootstrap; gmle

摘要

Censored data, where the event time is partially observed, are challenging for survival probability estimation. In this article, we introduce a novel nonparametric fiducial approach to interval-censored data, including right-censored, current status, case II censored, and mixed case censored data. The proposed approach leveraging a simple Gibbs sampler has a useful property of being one size fits all, that is, the proposed approach automatically adapts to all types of noninformative censoring mechanisms. As shown in the extensive simulations, the proposed fiducial confidence intervals significantly outperform existing methods in terms of both coverage and length. In addition, the proposed fiducial point estimator has much smaller estimation errors than the nonparametric maximum likelihood estimator. Furthermore, we apply the proposed method to Austrian rubella data and a study of hemophiliacs infected with the human immunodeficiency virus. The strength of the proposed fiducial approach is not only estimation and uncertainty quantification but also its automatic adaptation to a variety of censoring mechanisms. Supplementary materials for this article are available online.