| Summary: | 碩士 === 國立臺灣大學 === 農藝學研究所 === 101 === The non-inferiority test is an approach to assess the accuracy of a new diagnostic test if it reduces the cost.
Receiver operating characteristic (ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales.
The area under ROC curve (AUROC) is widely applied in many kinds of field and is used as a tool to decide the accuracy of diagnositic tests.
However, it may not differentiate the various shapes of the ROC curves with different diagnostic significances.
The partial area under ROC curve (PAUROC) is an attractive alternative that can provide additional and complimentary information for some
diagnostic procedures which require the false-positive rate (FPR) to be within a range of clinical interest.
A gold standard (GS) test on the true disease status is required to estimate the PAUROC.
However, a GS test may be too expensive or infeasible. In many medical researches, the true disease status of the subjects may remain unknown.
Under the normality assumption on test results from each disease group of subjects, we propose the maximum likelihood-based (ML-based) method
to construct a non-inferiority test for diagnostic accuracy based on the difference in paired PAUROCs in the absence of a GS test (NGS).
Simulation results shows that the proposed method for non-inferiority under the NGS case controls the size at the nominal level,
and the performance in empirical power is similar to GS case. The proposed method is illustrated with an example.
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