gsym.point constructs confidence intervals for the Generalized Symmetry point and its accuracy measures sensitivity and specificity for a continuous diagnostic test using two methods: the Generalized Pivotal Quantity method and the Empirical Likelihood method.
Default print method for objects fitted with gsym.point() function. A short summary is printed with: the call to the gsym.point() function for each categorical covariate level (if the categorical.cov argument of the gsym.point() function is not NULL).
Continuous biomarkers or diagnostic tests are often used to discriminate between healthy and diseased populations. In clinical practice, it is useful to select an appropriate cutpoint or discrimination value c which defines the positive and negative test results. Several methods for selecting optimal cutpoints in diagnostic tests in the sense of a specific optimality criterion have been proposed in the literature depending on the underlying reason for this choice (see for example, Youden, 1950; Pepe, 2003; Liu, 2012; Rota and Antolini, 2014). One of the best-known methods is based on the Symmetry point, also known in the literature as the point of equivalence (Greiner et al., 1995; Defreitas et al., 2004; Adlhoch et al., 2011), defined as the point where the sensitivity and specificity measures are equal. Taking into account the costs associated to the false positives and false negatives misclassifications, the Generalized Symmetry point can be defined. This package allows the user to compute the Generalized Symmetry point as the optimal cutpoint for a diagnostic test or continuous marker. The two methods introduced in L<c3><b3>pez-Rat<c3><b3>n et al. (2015) for estimating the Generalized Symmetry point and its sensitivity and specificity accuracy measures have been implemented in this package. One of them is based on the Generalized Pivotal Quantity (Weerahandi, 1993; 1995) and the other based on Empirical Likelihood (Thomas and Grunkemeier, 1975). Numerical and graphical outputs for these two methods are easily obtained.
Produces a summary of a gsym.point object. The following is printed: the matched call to the gsym.point() main function; the area under the ROC curve (AUC) estimate; the Generalized Symmetry point obtained with the method(s) selected and the point estimates of the associated sensitivity and specificity indexes with their corresponding confidence intervals. All this information will be shown for each categorical covariate level (if the categorical.cov argument in the gsym.point() function is not NULL).
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