Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

By M. Lee, G. A. Whitmore. 2006

302 citations recorded by the source corpus.

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.

Subfields: Safety, Risk, Reliability and Quality, Software, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0b3d4b591237a62376c11bbbd68a37da7d06f827

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Citations in the corpus

Residual life estimation based on bivariate Wiener degradation process with time-scale transformations (2014, background): “For more details about the first passage time, readers can refer to [11,12].”
Residual life estimation based on bivariate non-stationary gamma degradation process (2015, background): “For a discussion on a variety of FHT models and their potential, one may refer to [13].”
Inverse Tempered Stable Subordinators (2014, methodology): “MSC: Primary: 60E07; Secondary: 97K60, 40E05 1 Introduction The first-hitting time process (or the first passage time) arises naturally in diverse fields such as finance, insurance, process control and survival analysis (see e.g., Lee and Whitmore (2006)).”
Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “Recently , Lee and Whitmore ( 2006 ) and Balka et al .”
Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process (2012, methodology): “Lee and Whitmore [27] have given a comprehensive review of a variety of FHT models, and have discussed their potential.”