Profile-Likelihood Inference for Highly Accurate Diagnostic Tests

Tsimikas, John V.; Bosch, Ronald J.; Coull, Brent A.; Barmi, Hammou El

doi:10.1111/j.0006-341x.2002.00946.x

Cited by 9 publications

(23 citation statements)

References 17 publications

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“…That is, we assume that δ 1 =δ 2 = … δ L =δ, where δ l denotes the AUC for the l th institution. This is the same assumption made in Tsimikas et al (2002) when the response of a diagnostic test is categorical (ordinal rating).…”

Section: El Intervals For Auc With Stratified Samplesmentioning

confidence: 83%

“…Our proposed method is an additional contribution for constructing a confidence interval for the AUC of a highly accurate diagnostic test. When the response of a diagnostic test is ordinal, Tsimikas et al (2002) proposed a profile likelihood method for constructing such an interval. However, their method cannot be applied when the response of the test is continuous.…”

Section: Discussionmentioning

confidence: 99%

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Empirical Likelihood Inference for the Area under the ROC Curve

Qin

Zhou

2005

Biometrics

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Summary For a continuous‐scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the AUC and show that its limiting distribution is a scaled chi‐square distribution. We then obtain an EL‐based confidence interval for the AUC using the scaled chi‐square distribution. This EL inference for the AUC can be extended to stratified samples, and the resulting limiting distribution is a weighted sum of independent chi‐square distributions. Additionally we conduct simulation studies to compare the relative performance of the proposed EL‐based interval with the existing normal approximation‐based intervals and bootstrap intervals for the AUC.

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Section: El Intervals For Auc With Stratified Samplesmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Empirical Likelihood Inference for the Area under the ROC Curve

Qin

Zhou

2005

Biometrics

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“…Further, we partially compare the stated confidence intervals with an empirical likelihood (EL) approach proposed by Qin and Zhou () and recommended in Qin and Hotilovac (), as well as a profile likelihood approach found in Tsimikas et al. () . Thus, 11 different computation methods will be compared.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Moreover, the profile likelihood method of Tsimikas et al. () has been developed for very large AUC values close to 1 and seems to be the method of choice in such situation with discrete data.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Permutation‐based inference for the AUC: A unified approach for continuous and discontinuous data

2016

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We investigate rank-based studentized permutation methods for the nonparametric Behrens-Fisher problem, that is, inference methods for the area under the ROC curve. We hereby prove that the studentized permutation distribution of the Brunner-Munzel rank statistic is asymptotically standard normal, even under the alternative. Thus, incidentally providing the hitherto missing theoretical foundation for the Neubert and Brunner studentized permutation test. In particular, we do not only show its consistency, but also that confidence intervals for the underlying treatment effects can be computed by inverting this permutation test. In addition, we derive permutation-based range-preserving confidence intervals. Extensive simulation studies show that the permutation-based confidence intervals appear to maintain the preassigned coverage probability quite accurately (even for rather small sample sizes). For a convenient application of the proposed methods, a freely available software package for the statistical software R has been developed. A real data example illustrates the application.

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Score and profile likelihood confidence intervals for contingency table parameters

Lang

2008

Statistics in Medicine

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A straightforward approach to computing score and profile likelihood confidence intervals for contingency table parameters is described. The computational approach herein avoids two main limitations of existing methods: (1) Compared with existing methods, this paper's approach is applicable to a much broader class of parameters. (2) Unlike existing methods, this paper's approach is not case-specific and, hence, lends itself to a general, yet very simple, computational algorithm. This paper describes the 'sliding quadratic' computational algorithm and illustrates its use on examples that have not been previously considered in the literature. A small-scale simulation study highlights the advantage of using score and profile likelihood intervals rather than Wald intervals.

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Profile-Likelihood Inference for Highly Accurate Diagnostic Tests

Cited by 9 publications

References 17 publications

Empirical Likelihood Inference for the Area under the ROC Curve

Empirical Likelihood Inference for the Area under the ROC Curve

Permutation‐based inference for the AUC: A unified approach for continuous and discontinuous data

Score and profile likelihood confidence intervals for contingency table parameters

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