| Summary: | Nonparametric Predictive Inference (NPI) is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, with inferences in terms of one or more future observations. NPI has been introduced for diagnostic test accuracy, yet mostly restricting attention to one future observation. In this thesis, NPI for the accuracy of diagnostic tests will be developed for multiple future observations. The present thesis consists of three main contributions related to studying the accuracy of diagnostic tests. We introduce NPI for selecting the optimal diagnostic test thresholds for two-group and three-group classification, and we compare two diagnostic tests for multiple future individuals. For the two- and three-group classification problems, we present new NPI approaches for selecting the optimal diagnostic test thresholds based on multiple future observations. We compare the proposed methods with some classical methods, including the two-group and three-group Youden index and the maximum area (volume) methods. The results of simulation studies are presented to investigate the predictive performance of the proposed methods along with the classical methods, and example applications using data from the literature are used to illustrate and discuss the methods. NPI for comparison of two diagnostic tests is presented, assuming the tests are applied on the same individuals from two groups, namely healthy and diseased individuals. We also introduce weights to reflect the relative importance of the two groups.
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