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Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305605
Title: Statistical Inference for the Treatment Effect in Functional AFT Model with Longitudinal Trajectories of Time-Varying Covariate
Author(s): Jincheng Shen*+ and Lu Wang and Jeremy Michael George Taylor
Companies: University of Michigan and University of Michigan and University of Michigan
Address: M4048B SPHII, Ann Arbor, MI, 48109, United States
Keywords: AFT model ; functional analysis ; treatment by indication

Typical survival models that include longitudinal covariates as time-varying variables only use the information at event times and assume the hazard rate at a given time is determined by current covariate levels. We consider a situation where the hazard rate may depend on the entire history as captured by the covariate trajectory. Motivated by a prostate cancer recurrence study, we investigate estimating the coefficient for the treatment effect on cancer recurrence while also including PSA in the model. Specifically, we propose a partial functional accelerated failure time model, where the treatment variable is included in the model parametrically, and the whole history of individual longitudinal PSA trajectory is incorporated as a functional part. The asymptotic properties of the estimated treatment effects are derived using empirical process theory. We demonstrate the finite sample performance by simulation studies, and illustrate the method using the prostate cancer data.

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