Title of article
The diagnostic accuracy of a composite index increases as the number of partitions of the components increases and when specific weights are assigned to each component
Author/Authors
Georgia Kourlaba & Demosthenes Panagiotakos، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2010
Pages
18
From page
537
To page
554
Abstract
The aim of this work was to evaluate whether the number of partitions of index components and the use of
specific weights for each component influence the diagnostic accuracy of a composite index. Simulation
studies were conducted in order to compare the sensitivity, specificity and area under the ROC curve
(AUC) of indices constructed using equal number of components but different number of partitions for
all components. Moreover, the odds ratio obtained from the univariate logistic regression model for each
component was proposed as potential weight. The current simulation results showed that the sensitivity,
specificity andAUC of an index increase as the number of partitions of components increases. However, the
rate that the diagnostic accuracy increases is reduced as the number of partitions increases. In addition, itwas
found that the diagnostic accuracy of the weighted index developed using the proposed weights is higher
compared with that of the corresponding un-weighted index. The use of large-scale index components
and the use of effect size measures (i.e. odds ratios, ORs) of index components as potential weights are
proposed in order to obtain indices with high diagnostic accuracy for a particular binary outcome.
Keywords
AUC , simulations , Application , Weights , Indices , specificity
Journal title
JOURNAL OF APPLIED STATISTICS
Serial Year
2010
Journal title
JOURNAL OF APPLIED STATISTICS
Record number
712411
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