Stats4Grads: Nonparametric predictive inference (NPI) with copula for bivariate diagnostics test results
16 December 2015 13:00 in CM105
The Receiver Operating Characteristic (ROC) curve is a common statistical tool to measure the accuracy of a diagnostic test that yields ordinal or continuous results. It is increasingly clear that in medical settings, one test result (biomarker) will not be sufficient to serve as screening device for early detection of many diseases and may be very costly. Many researchers believe that a combination of test results will potentially lead to more sensitive screening rules for detecting diseases.
In this study we present a new linear combination of two test results by considering the dependence structure, by combining Nonparametric Predictive Inference (NPI) for the marginals with copulas to take dependence into account. Our method uses a discretized version of the copula which fits perfectly with the NPI method for the marginals and leads to relatively straightforward computations because there is no need to estimate the marginals and the copula simultaneously.
We investigate and discuss the performance of this method by presenting results from simulation studies. The method is further illustrated via application in real data sets from the literature. We also briefly outline related challenges and opportunities for future research.
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