Confidence interval estimation of the difference between two sensitivities to the early disease stage

Dong, Tuochuan; Kang, Le; Hutson, Alan D.; Xiong, Chengjie; Tian, Lili

doi:10.1002/bimj.201200012

Cited by 7 publications

(8 citation statements)

References 38 publications

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“…For future work, following the same vein of Dong et al (2014), we would like to develop the semi-parametric inference procedure for the difference of two correlated P 2 ’s, based on the empirical likelihood technique.…”

Section: Summary and Discussionmentioning

confidence: 99%

Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood

Dong

Tian

2014

Journal of Biopharmaceutical Statistics

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Many disease processes can be divided into three stages: i.e. the non-diseased stage, the early diseased stage and the fully diseased stage. To assess the accuracy of diagnostic tests for such diseases, various summary indexes have been proposed, such as volume under the surface (VUS), partial volume under the surface (PVUS), and the sensitivity to the early diseased stage given specificity and the sensitivity to the fully diseased stage (P2). This paper focuses on confidence interval estimation for P2 based on empirical likelihood. Simulation studies are carried out to assess the performance of the new methods compared to the existing parametric and non-parametric ones. A real data set from Alzheimer’s Disease Neuroimaging Initiative (ANDI)2 is analyzed. Key Words: Empirical Likelihood; Diagnostic tests; The sensitivity to the early diseased stage.

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Section: Summary and Discussionmentioning

confidence: 99%

Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood

Dong

Tian

2014

Journal of Biopharmaceutical Statistics

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“…Note that the transformation parameters λ 1 and λ 2 have to be estimated by the data; hence, their variability must be taken into account. This issue is usually ignored in the literature when the Box‐Cox is considered (see the work of Dong et al among others). The underlying likelihood is of the form

\begin{matrix} alignleft \\ align-1 L (p^{false(λ_{1, 2} false)}) & align-2 = \prod_{i = 1}^{n_{A}} \frac{1}{2 π \sqrt{\det (Σ_{A}^{false(λ_{1, 2} false)})}} \exp (- \frac{1}{2} (W_{1 A i}^{(λ_{1})} - μ_{1 A}^{(λ_{1})}, W_{2 A i}^{(λ_{2})} - μ_{2 A}^{(λ_{2})}) {boldΣ}_{A}^{{(λ_{1, 2})}^{- 1}} {(W_{1 A i}^{(λ_{1})} - μ_{1 A}^{(λ_{1})}, W_{2 A i}^{(λ_{2})} - μ_{2 A}^{(λ_{2})})}^{'}) \\ align-1 & align-2 \times \prod_{j = 1}^{n_{B}} \frac{1}{2 π \sqrt{\det (Σ_{B}^{false(λ_{1, 2} false)})}} \exp (- \frac{1}{2} (W_{1 B j}^{(λ_{1}})) \end{matrix}

…”

Section: Delta‐based Box‐cox Approachmentioning

confidence: 99%

“…The size is very close to the true nominal level in all cases (see Table 1 in Web Appendix A). We compared the delta‐based approach where the distributional assumptions are correct, under the same setting, with the approach of Dong et al, which is based on generalized pivots (see Table 2 in Web Appendix A). We observed that the proposed method consistently outperforms the generalized pivot‐based approach in terms of size.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…In such situations, for a clinically appealing comparison of two markers, it might be necessary to fix a pair of any true classification rates and focus on making comparisons and inference for the third true classification rate that refers to the remaining group. Resampling‐based approaches that employ generalized pivots have been investigated when only a specific true classification rate is of interest (see the works of Dong et al).…”

Section: Introductionmentioning

confidence: 99%

“…We further explore a nonparametric kernel‐based method. We use simulations to compare our approaches with the generalized pivot‐based approach of the work of Dong et al, who only discussed comparisons with respect to T C B . This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of two correlated ROC surfaces at a given pair of true classification rates

Bantis

Feng

2018

Statistics in Medicine

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The receiver operating characteristic (ROC) curve is typically employed when one wants to evaluate the discriminatory capability of a continuous or ordinal biomarker in the case where two groups are to be distinguished, commonly the "healthy" and the "diseased." There are cases for which the disease status has three categories. Such cases employ the ROC surface, which is a natural generalization of the ROC curve for three classes. In this paper, we explore new methodologies for comparing two continuous biomarkers that refer to a trichotomous disease status, when both markers are applied to the same patients. Comparisons based on the volume under the surface have been proposed before, but that measure is often not clinically relevant. Here, we focus on comparing two correlated ROC surfaces at given pairs of true classification rates, which are more relevant to patients and physicians. We propose delta-based parametric techniques, power transformations to normality, and bootstrap-based smooth nonparametric techniques to investigate the performance of an appropriate test. We evaluate our approaches through an extensive simulation study and apply them to a real dataset from prostate cancer screening.

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Empirical likelihood confidence interval for sensitivity to the early disease stage

Rahman

Zhao

2021

Pharmaceutical Statistics

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Disease status can naturally be classified into three or more ordinal stages rather than just being binary stages. Many works have been done for the estimation and inference procedure regarding three ordinal disease stages, which are non‐disease, early disease, and full disease stages. The early disease stage can be very important for therapeutic intervention and prevention potentiality. As a diagnostic measure, sensitivity to the early disease stage is often used. In this article, we propose confidence intervals for the sensitivity to early disease stage based on given target specificity for non‐disease stage and target sensitivity to full disease stage using both empirical likelihood (EL) and adjusted EL procedures. We compare the performance of the proposed EL confidence intervals with other procedures in our simulation study. The proposed procedures are further applied to Alzheimer's Disease Neuroimaging Initiative data set.

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Confidence interval estimation of the difference between two sensitivities to the early disease stage

Cited by 7 publications

References 38 publications

Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood

Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood

Comparison of two correlated ROC surfaces at a given pair of true classification rates

Empirical likelihood confidence interval for sensitivity to the early disease stage

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