Statistical inference on shape and size indexes for counting processes

• 成果类型: Article
• 署名作者: Sun, Yifei; Chiou, Sy Han; Marr, Kieren A.; Huang, Chiung-Yu
• 署名单位: Columbia University; University of Texas System; University of Texas Dallas; Johns Hopkins University; University of California System; University of California San Francisco
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asab008
• 发表日期: 2022
• 页码: 195208
• 关键词: recurrent event processes; SEMIPARAMETRIC ANALYSIS; Nonparametric analysis; regression-models; estimators

摘要

Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. We propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the size and shape components, the proposed model possesses the desired directional interpretability of covariate effects and encompasses many commonly used models as special cases. To tackle the analytical challenges arising from leaving the two link functions unspecified, we develop a two-step rank-based estimation procedure to estimate the regression parameters with or without informative censoring. The proposed estimators are asymptotically normal, with a root-n convergence rate. To guide model selection, we develop hypothesis testing procedures for checking shape and size independence. Simulation studies and a data example on a hematopoietic stem cell transplantation study are presented to illustrate the proposed methodology.