JSM 2005 - Toronto

Abstract #302712

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 498
Type: Invited
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302712
Title: Methods for Categorizing a Prognostic Variable in a Multivariable Setting
Author(s): Alex Smith*+ and Madhu Mazumdar and Jennifer Bacik
Companies: Pacific Data Designs, Inc. and Weill Medical College of Cornell University and Memorial Sloan-Kettering Cancer Center
Address: 900 North Point Street Suite C180, San Francisco, CA, 94109, United States
Keywords: Split Sample Approach ; 2-Fold Cross-Validation Approach ; Log-likelihood Statistic
Abstract:

The literature is filled with examples of categorization of a continuous prognostic variable in a univariate setting followed by the addition of this categorical variable to an existing multivariable model. Typically, an "optimal" cutpoint for a new prognostic factor is obtained through a systematic search relating the variable to the outcome in a univariate manner. The corresponding categorical variable is then fitted using a multivariable model with other already established prognostic covariates to assess the additional value of the new factor. This prompts the question of whether the cutpoint search should have been performed in the same multivariable setting in which it will be used. We extend the univariate cutpoint analysis methods (split sample and cross-validation approaches) to the multivariable setting using a log-likelihood statistic as the correlative measure. A Monte Carlo simulation demonstrates that both methods are more efficient in detecting the true cutpoint and in estimating the effect size under the multivariable setting. The crossvalidation method performs better than the split sample method in univariable as well as multivariable scenarios.


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Revised March 2005