Longitudinal Survival Analysis Using First Hitting Time Threshold Regression: With Applications to Wiener Processes

Ya-Shan Cheng, Yiming Chen and Mei-Ling Ting Lee ()
Additional contact information
Ya-Shan Cheng: Institute of Statistics, National Tsing Hua University, Hsinchu 300044, Taiwan
Yiming Chen: Food and Drug Administration, Silver Spring, MD 20993, USA
Mei-Ling Ting Lee: Epidemiology and Biostatistics Department, University of Maryland, College Park, MD 20742, USA

Stats, 2025, vol. 8, issue 2, 1-16

Abstract: First-hitting time threshold regression (TR) is well-known for analyzing event time data without the proportional hazards assumption. To date, most applications and software are developed for cross-sectional data. In this paper, using the Markov property of processes with stationary independent increments, we present methods and procedures for conducting longitudinal threshold regression (LTR) for event time data with or without covariates. We demonstrate the usage of LTR in two case scenarios, namely, analyzing laser reliability data without covariates, and cardiovascular health data with time-dependent covariates. Moreover, we provide a simple-to-use R function for LTR estimation for applications using Wiener processes.

Keywords: degradation; latent model; Markov decomposition; overshoot; reliability; semiparametric model; Wiener process (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/8/2/32/pdf (application/pdf)
https://www.mdpi.com/2571-905X/8/2/32/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:8:y:2025:i:2:p:32-:d:1644920

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().