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

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 and AUC 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, it was 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.