Detection of Multiple Change Points in an Accelerated Failure Time Model Using Sequential Testing

• Activity Number: 404 - Recent Research in High-Dimensional and Complex Data Analysis
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Kayoung Park* and Kristine Gierz
• Companies: Old Dominion University and Old Dominion University
• Keywords: accelerated failure time model; change point analysis; censored data; likelihood ratio test; sequential testing

Abstract

With improvements to cancer diagnoses and treatments, incidences and mortality rates have changed. However, the most commonly used analysis methods do not account for such distributional changes. In survival analysis, change point problems can concern a shift in a distribution for a set of time-ordered observations, potentially under censoring or truncation. We propose a sequential testing approach for detecting multiple change points in the Weibull accelerated failure time model, since this is sufficiently flexible to accommodate increasing, decreasing, or constant hazard rates and is also the only continuous distribution for which the accelerated failure time model can be reparametrized as a proportional hazards model. Our sequential testing procedure does not require the number of change points to be known; this information is instead inferred from the data. We conduct a simulation study to show that the method accurately detects change points and estimates the model. The numerical results along with a real data application demonstrate that our proposed method can detect change points in the hazard rate.