Threshold Regression for Analysis of Time-to-event Data: With Connection to Proportional Hazard Model and Applications


Mei-Ling Lee

11:00:00 - 11:50:00

308 , Mathematics Research Center Building (ori. New Math. Bldg.)

Threshold regression (TR) is an alternative methodology for analysis of time-to-event data. The method is not built on consideration of hazard functions. Threshold regression methodology is based on the concept that the degradation of a machine or a patient’s health status follows a stochastic process. For engineering applications, the degradation can often be observed. For medical research, a patient’s health status is a complex unobservable process. The onset of disease, or death, occurs when the process first reaches a failure threshold (i.e., a first hitting time). Instead of calendar time, analytical time (also called operational time) can be included in TR regression. The TR model is intuitive and does not require the proportional hazards (PH) assumption. It thus provides an important alternative for analyzing survival data. We will discuss both parametric models and distribution-free methods developed for threshold regression. Also, we will discuss the connections between the TR and PH regression methodologies.