On the exact interval estimation for the difference in paired areas under the ROC curves

Li, Chi–Rong; Liao, Chen‐Tuo; Liu, Jen‐pei

doi:10.1002/sim.2760

Cited by 40 publications

(33 citation statements)

References 32 publications

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“…The concepts of generalized confidence interval and generalized p -value have been successfully applied to a variety of practical settings where standard exact solutions do not exist for confidence intervals and hypothesis testing. It has been shown that generalized inference approaches generally have good performance, even at small sample sizes (see e.g., Weerahandi, 1995; Weerahandi and Berger, 1999; Krishnamoorthy and Lu, 2003; Tian and Cappelleri, 2004; Tian, 2008; Li et al, 2008a, 2008b). …”

Section: Parametric Approachesmentioning

confidence: 99%

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

et al. 2013

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Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer’s disease (AD) is analyzed using the proposed approaches.

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Section: Parametric Approachesmentioning

confidence: 99%

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

et al. 2013

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“…Tsui and Weerahandi [5], and Weerahandi [6] propose the generalized confidence interval (GCI) based on the generalized pivotal quantity (GPQ) for the exact statistical inference. It has been successfully applied to various areas including population and individual bioequivalence [7], tolerance intervals for quality control [8,9], and the area under the receiver operating characteristic (ROC) curve [10,11]. As a result, we propose to apply the concept of GCI for validation of quantitative analytical laboratory procedures.…”

Section: Introductionmentioning

confidence: 98%

Statistical methods for evaluating the linearity in assay validation

Hsieh

Hsiao

Liu

2008

Journal of Chemometrics

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One of the most important characteristics for evaluation of the accuracy in assay validation is the linearity. Kroll, et al.[1] proposed a method based on the average deviation from linearity (ADL) to evaluate the linearity. Hsieh and Liu [2] suggested that hypothesis for proving the linearity be formulated as the alternative hypothesis and proposed the corrected Kroll's method. However, the issue concerning the variability in estimation of the non-centrality parameter is still unresolved. Consequently, the type I error rates may still be inflated for the corrected Kroll's method. To overcome this issue, we propose the sum of squares of deviations from linearity (SSDL) as an alternative metric for evaluation of linearity. Based on SSDL, we applied the method of generalized pivotal quantities (GPQ) for the inference of evaluation of linearity. The simulation studies were conducted to empirically investigate the size and power between current and proposed methods. The simulation results show that the proposed GPQ method not only adequately control size but also provide sufficient power than other methods. A numeric example illustrates the proposed methods.

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“…While the conventional frequentist methods fail to provide satisfactory solutions for interval estimation because of complex parametric functions situations, GPQs can serve as tractable means of providing GCIs. The GCIs have been applied successfully to many biological studies such as occupational exposure limit [9,10]; population and individual bioequivalence, [16]; accuracy comparison with receiver operating characteristic curves [13]; tolerance intervals for random effects models [14]; etc. The simulation studies presented in the aforementioned literatures strongly support that the coverage probabilities of GCI approach perform well especially in small sample sizes.…”

Section: Introductionmentioning

confidence: 99%

Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data

Hsieh

2014

Journal of Applied Statistics

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Highly skewed and non-negative data can often be modeled by the delta-lognormal distribution in fisheries research. However, the coverage probabilities of extant interval estimation procedures are less satisfactory in small sample sizes and highly skewed data. We propose a heuristic method of estimating confidence intervals for the mean of the delta-lognormal distribution. This heuristic method is an estimation based on asymptotic generalized pivotal quantity to construct generalized confidence interval for the mean of the delta-lognormal distribution. Simulation results show that the proposed interval estimation procedure yields satisfactory coverage probabilities, expected interval lengths and reasonable relative biases. Finally, the proposed method is employed in red cod densities data for a demonstration.

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On the exact interval estimation for the difference in paired areas under the ROC curves

Cited by 40 publications

References 32 publications

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

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

Statistical methods for evaluating the linearity in assay validation

Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data

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