Journal of Applied Mathematics and Physics

Volume 3, Issue 7 (July 2015)

ISSN Print: 2327-4352   ISSN Online: 2327-4379

Google-based Impact Factor: 0.70  Citations  

Optimal Weights in Nonparametric Analysis of Clustered ROC Curve Data

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DOI: 10.4236/jamp.2015.37102    6,266 Downloads   7,372 Views  
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ABSTRACT

In diagnostic trials, clustered data are obtained when several subunits of the same patient are observed. Within-cluster correlations need to be taken into account when analyzing such clustered data. A nonparametric method has been proposed by Obuchowski (1997) to estimate the Receiver Operating Characteristic curve area (AUC) for such clustered data. However, Obuchowski’s estimator gives equal weight to all pairwise rankings within and between cluster. In this paper, we modify Obuchowski’s estimate by allowing weights for the pairwise rankings vary across clusters. We consider the optimal weights for estimating one AUC as well as two AUCs’ difference. Our results in this paper show that the optimal weights depends on not only the within-patient correlation but also the proportion of patients that have both unaffected and affected units. More importantly, we show that the loss of efficiency using equal weight instead of our optimal weights can be severe when there is a large within-cluster correlation and the proportion of patients that have both unaffected and affected units is small.

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Wu, Y. (2015) Optimal Weights in Nonparametric Analysis of Clustered ROC Curve Data. Journal of Applied Mathematics and Physics, 3, 828-834. doi: 10.4236/jamp.2015.37102.

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