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Activity Number: 697
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #316961
Title: The Historical Cox Model
Author(s): Jonathan Gellar* and Fabian Scheipl and Mei-Cheng Wang and Dale Needham and Ciprian Crainiceanu
Companies: Mathematica Policy Research and Ludwig Maximilians University Munich and The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University
Keywords: Survival Analysis ; Time-varying covariates ; Functional data analysis ; Semiparametric regression ; Longitudinal data analysis ; Smoothing
Abstract:

In this paper, we extend the Cox proportional hazards model to account for densely sampled time-varying covariates as historical functional terms. This approach allows the hazard function at any time t to depend not only on the current value of the time-varying covariate, but also on all previous values. The fundamental idea is to assume a bivariate coefficient function ?(s, t) that estimates a weight function that is applied to the full or partial covariate history up to t, and is allowed to change with t. Estimation is performed by maximizing the penalized partial likelihood, using a likelihood-based information criterion to optimize the smoothing parameter. Methods are applied to a study of in-hospital mortality among patients with acute respiratory distress syndrome in the intensive care unit.


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

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