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In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers' accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures. [ABSTRACT FROM AUTHOR]

This paper proposes to use the convex combination of several kernel estimators based on the ranked set sampling (RSS) scheme for estimating the underlying distribution function to construct more efficient quantiles estimation. Several different estimators of quantile function based on the simple random sampling and ranked set sampling are provided. The variance and mean-squared error of the proposed estimators are derived as explicit forms. Theoretical derivation and an intensive simulation study are used to compare the performance of the proposed method with other methods in the literature. The effect of imperfect rankings on the performance of the proposed estimators is investigated. Real data example from the China Health and Nutrition Survey is used to illustrate our proposed approach. Some discussions using another two real data are presented. It observes that the proposed estimators have different behaviours with respect to symmetric and asymmetric distributions. [ABSTRACT FROM AUTHOR]

The evaluation of diagnostics tests based on net benefit involves both the accuracy of the tests and the clinical consequences of the diagnostic errors. Also, the benefit-risk measures approach depends on the prevalence of the underlying disease. However, for some diseases or clinical conditions, the prevalence is either unknown or different from region to region or population to population, resulting in an erroneous diagnosis. This paper introduces innovative post-test diagnostic accuracy measures and a new cut-point selection criterion based on the expected relative net benefit. Our approach does not depend on the disease's prevalence, maximizing net benefit and reducing the clinical consequences of the diagnostic errors. We demonstrate the advantages of the proposed measures to compare different diagnostic tests and/or biomarkers, on average, the abilities for rule-in, rule-out clinical condition, and as cut-point selection criterion that maximize the expected relative net benefit diagnostic accuracy. Numerical examples, simulation studies, and real data are provided to illustrate the superiority and applicability of the proposed measures. [ABSTRACT FROM AUTHOR]

The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers. [ABSTRACT FROM AUTHOR]

The kernel-based estimator of Cochran Mantel-Haenszel odds ratio based on stratified simple and ranked set sampling is proposed. The expectation and variance of the estimator are analytically obtained. Using a simulation study, the estimator based on stratified ranked set sampling is more efficient than its counterpart based on stratified simple random sampling. Finally, the estimator's performance is investigated by using base deficit data. [ABSTRACT FROM AUTHOR]

This paper proposes to extend [1] mean functional estimation method based on the influential exponential tilting resampling approach (ITRA) to address non-ignorable missing data in linear model parameters statistical inference. The ITRA approach assumes that the nonrespondents' model corresponds to an exponential tilting of the respondents' model. The tilted model's specified function is the influential function of the function of interest (parameter). The other basis of the proposed approach is to use the importance resampling techniques to draw inferences about some linear model parameters. Simulation studies were conducted to investigate the performance of the proposed methods and their application to real data. Theoretical justifications are provided as well. [ABSTRACT FROM AUTHOR]

Positive predicted value and negative predicted value are used by clinicians to evaluate how likely a disease stage is present given the test results. In contrast, positive and negative likelihood ratios (LRs) are used in practice to assess the potential utility of a specific diagnostic test and the likelihood of a patient having the condition. This article introduces the concepts and the derivation of generalized predictive values and LRs from binary diseases to ordinal multistages diseases. We evaluate the performance of the proposed methods with numerical examples. We illustrated the proposed methods provided using real data. [ABSTRACT FROM AUTHOR]