| 摘要(英) |
In this paper, we consider testing the non-inferiority of two medical
diagnostic methods in a case-control study where each subject receiving the two
different diagnostics produces correlated paired measurements. Note that it
occurs often in practice that the marginal distributions of the measurements are
right-skewed. Therefore, we first apply the power transformation to the
paired data so that they would behave like the bivariate normal data. One
parametric non-inferiority test is then implemented based on the transformed
data. On the other hand, we suggest and employ appropriate copula function
which links two generalized gamma distributions to describe the joint
distribution of the paired measurements. Under the joint distribution, an
approximate test based on the difference between the partial areas under the two
estimated Receiver Operating Characteristic (ROC) curves is then constructed.
In this paper, we would like to test if the difference between the true areas is
within an allowable region. The results of a simulation investigation of the
level and power performances of the approximate test for different degrees of
correlation in several possible copula functions with a variety of marginal
distributions are reported. Finally, a real data set is illustrated by using the
approximate test.
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| 參考文獻 |
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