This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 458
Type: Topic Contributed
Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #309103
Title: Assessing the Goodness of Fit of Risk Prediction Rules in a Clustered Data Setting
Author(s): Bernard Rosner*+ and Weiliang Qiu and MeiLing Ting Lee
Companies: Harvard Medical School and Harvard Medical School and University of Maryland
Address: 181 Longwood Avenue, Boston, MA, 02115,
Keywords: Risk prediction ; ROC curves ; clustered data ; GEE

The AUC is a commonly used metric to assess discrimination of risk prediction rules; however, standard errors of AUC are usually based on the Mann-Whitney U test that requires independence of sample units. For ophthalmologic applications, it is desirable to assess risk prediction rules based on eye-specific outcome variables which are generally highly , but not perfectly correlated in fellow eyes (eg. progression of individual eyes to AMD). In this article, we use the extended Mann-Whitney U test (Rosner et al, 2009) for the case where subunits within a cluster may have different progression status and assess goodness of fit of different prediction rules in this setting. Both data analyses based on progression of AMD and simulation studies show reasonable accuracy of this extended Mann-Whitney U test to assess the goodness of fit of eye-specific risk prediction rules.

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