Assessing discrimination of risk prediction rules in a clustered data setting.

ABSTRACT: The AUC (area under ROC curve) 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 assumes independence of sampling 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 [e.g. progression of individual eyes to age-related macular degeneration (AMD)]. In this article, we use the extended Mann-Whitney U test (Rosner and Glynn, Biometrics 65:188-197, 2009) for the case where subunits within a cluster may have different progression status and assess discrimination of different prediction rules in this setting. Both data analyses based on progres

SUBMITTER: Rosner B 

PROVIDER: S-EPMC3622772 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature