Empirical Likelihood Based Confidence Intervals for the Difference between Two Sensitivities of Continuous-scale Diagnostic Tests at a Fixed Level of Specificity
نویسندگان
چکیده
Diagnostic testing is essential to distinguish non-diseased individuals from diseased individuals. The sensitivity and specificity are two important indices for the diagnostic accuracy of continuous-scale diagnostic tests. If we want to compare the effectiveness of two tests, it is of interest to construct a confidence interval for the difference of the two sensitivities at a fixed level of specificity. In this thesis, we propose two empirical likelihood based confidence intervals (HBELI and HBELII) for the difference of two sensitivities at a predetermined specificity level. Simulation studies show that when correlation between the two test results exists, HBELI and HBELII intervals perform better than the existing bootstrap based BCa, BTI and BTII intervals due to shorter interval lengths. However, when there is no correlation, BCa, BTI and BTII intervals outperform HBELI and HBELII intervals due to better coverage probability in most simulation settings. INDEX WORDS: Empirical likelihood, diagnostic test, sensitivity, specificity Empirical Likelihood Based Confidence Intervals for the Difference between Two Sensitivities of Continuous-scale Diagnostic Test at a Fixed Level of Specificity
منابع مشابه
New confidence intervals for the difference between two sensitivities at a fixed level of specificity.
For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper, we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute. We also conduct simulation studies to compare the relative performance of the new intervals with the ex...
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