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Activity Number: 412
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306053
Title: Explained Variation for the Proportional Hazards Mixed-Effects Model
Author(s): Gordon Honerkamp-Smith*+
Address: 4435 Nobel Dr, San Diego, CA, 92122, United States
Keywords: survival ; Cox ; proportional ; hazard ; explained ; variation

The notion of explained variation quantifies the extent to which a given model accounts for variation observed in the data. The R-squared coefficient in linear regression is perhaps the most well-known example, but such measures have been proposed for many other settings, including linear mixed-effects models and the Cox proportional hazards model in Survival Analysis. In this paper, we propose several measures of explained variation for the proportional hazards mixed-effects model--a powerful generalization of the Cox model proposed by Xu and Vaida in 2000. We examine their properties and compare their performance on simulated data, and demonstrate their usefulness for model selection using a lung-cancer multi-center clinical trials data set.

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