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Activity Number: 466 - First-Hitting-Time Based Threshold Regression and Applications
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract #328489 Presentation
Title: Bayesian Semiparametric Threshold Regression
Author(s): Jonathan Race* and Michael Pennell
Companies: and Ohio State University
Keywords: Threshold regression; Bayesian; Semi-parametric; Survival analysis; Dirichlet process mixture

Threshold regression (TR) is a relatively new method for survival analysis which assumes a latent health process underlies time-to-event. A common approach is to model the latent health process using a Wiener process with drift resulting in an inverse Gaussian distribution for time-to-event. TR has several advantages over more traditional parametric and semiparametric regression models including biologically meaningful covariate effects and the ability to model nonproportional hazards. However, the restrictive assumption of an inverse Gaussian distribution could lead to poor fit in certain scenarios. For instance, the model may fit poorly due to unexplained heterogeneity in the initial state and drift of the Wiener process. To address this limitation we propose modeling time-to-event using a Dirichlet Process Mixture of inverse Gaussians. Two different types of models are considered: one which models heterogeneity in the drift and another that simultaneously models heterogeneity in the drift and initial state. Posterior inference proceeds using a blocked Gibbs sampler. The methods are applied to data from a two year carcinogenicity study by the National Toxicology Program.

Authors who are presenting talks have a * after their name.

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