Jackknife empirical likelihood confidence regions for the evaluation of continuous-scale diagnostic tests with verification bias

Recently, Wang and Qin proposed various bias-corrected empirical likelihood confidence regions for any two of the three parameters, sensitivity, specificity, and cut-off value, with the remaining parameter fixed at a given value in the evaluation of a continuous-scale diagnostic test with verification bias. In order to apply those methods, quantiles of the limiting weighted chi-squared distributions of the empirical log-likelihood ratio statistics should be estimated. In order to facilitate application and reduce computation burden, in this paper, jackknife empirical likelihood-based methods are proposed for any pairs of sensitivity, specificity and cut-off value, and asymptotic results can be derived accordingly. The proposed methods can be easily implemented to construct confidence regions for the evaluation of continuous-scale diagnostic tests with verification bias. Simulation studies are conducted to evaluate the finite sample performance and robustness of the proposed jackknife empirical likelihood-based confidence regions in terms of coverage probabilities. Finally, a real case analysis is provided to illustrate the application of new methods.