JSM 2004 - Toronto

Abstract #301177

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Activity Number: 81
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract - #301177
Title: A Decision-tree-model Based on Roc Analysis Predicting Progression to Type 1 Diabetes Mellitus
Author(s): Shui Yu*+ and Sati Mazumdar and Nancy B. Sussman and Vincent C. Arena and Dorothy J. Becker
Companies: University of Pittsburgh and University of Pittsburgh and University of Pittsburgh and University of Pittsburgh and University of Pittsburgh
Address: Dept. of Biostatistics, Pittsburgh, PA, 15261,
Keywords: diabetes ; prediction ; tree-model
Abstract:

A novel decision tree analysis was used to select the factors that best predict the risk of Type 1 diabetes (T1DM) in first-degree relatives of T1DM probands. The uniqueness of this approach lies in the application of the receiver operating characteristic (ROC) curve analysis to identify the cutpoints for the predictors included in the model. This approach can be easily adjusted to give desired importance to sensitivity or specificity depending on the clinical relevance of the risk. The study was carried out in a cohort of 1484 first-degree relatives (FDRs) of T1DM probands followed in the greater Pittsburgh area for 22 years. We compared the model from this analysis with those from logistic regression and Cox model.

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