Detection of Multiple Change-Points in Hazard Models

• Activity Number: 566
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Risk Analysis
• Author(s): Wei Zhang*+ and Lianfen Qian and Yunxia Li
• Companies: Cornell University and Florida Atlantic University and Financial Institute of Zhejiang
• Keywords: Change-points ; Survival analysis ; Failure time ; Hazard rate ; Parameters estimation

Abstract

Change-point detection in hazard rate function is an important research topic in survival analysis. In this paper, we firstly review existing methods for single change-point detection in piecewise exponential hazard model. Then we consider the problem of estimating the change point in the presence of right censoring and long-term survivors while using Kaplan-Meier estimator for the susceptible proportion. The maximum likelihood estimators are shown to be consistent. Taking one step further, we propose an counting process based and least squares based change-point detection algorithm. For single change-point case, consistency results are obtained. We then consider the detection of multiple change-points in the presence of "long-term survivors" via maximum likelihood based and counting process based method. Last but not least, we use a weighted least squares based and counting process based method for detection of multiple change-points with long-term survivors and covariates. For multiple change-points detection, simulation studies show good performances of our estimators under various parameters settings for both methods. All methods are applied to real data analyses.