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Abstract Details

Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #306707
Title: Validating a Cox Proportional-Hazards Model
Author(s): Tristan Grogan*+ and David Elashoff
Companies: University of California at Los Angeles and University of California at Los Angeles
Address: 2600 Overland Ave, Los Angeles, CA, 90064, United States
Keywords: Cox proportional-hazards ; Validation ; Survival data analysis ; biomarkers

While several methods have been published on validating standard logistic or linear models, much less material exists on validating time-to-event models, such as the Cox proportional-hazards model. During the course of this research, an investigation of four different strategies for validating the Cox model was carried out, utilizing data from a prostate cancer study. These validation techniques are especially important for biomarker studies to aid in combating the effect of selection bias, and would strengthen the results and credibility of any study.

This paper will present four performance measures for assessing whether a model has evidence for being validated or not-comparing model coefficients between training and test data sets, assessing Harrell's c-index between the training and test models, running a cross-validation technique, and comparing recurrence risk predictions between the training and test models.

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