Direct estimation of volume under the ROC surface with verification bias.

In practice, the receiver operating characteristic (ROC) curve of a diagnostic test is widely used to show the performance of the test for discriminating two-class events. The area under the ROC curve (AUC) is proposed as an index for the assessment of the diagnostic accuracy of the test under consideration. Due to ethical and cost considerations associated with application of gold standard (GS) tests, only a subset of the patients initially tested have verified disease status.

Bayesian and influence function-based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests.

In practice, a disease process might involve three ordinal diagnostic stages: the normal healthy stage, the early stage of the disease, and the stage of full development of the disease. Early detection is critical for some diseases since it often means an optimal time window for therapeutic treatments of the diseases. In this study, we propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to construct confidence/credible intervals for the sensitivity of a test to patients in the early diseased stage given a specificity and a sensitivity of the test to patients in the fully diseased stage.

Infrared spectrometric biomarkers for ulcerative colitis screening using human serum samples.

This study uses infrared spectrometry coupled with data analysis techniques to understand colitis-induced alterations in the molecular components of serum samples. Using samples from 18 ulcerative colitis patients and 28 healthy volunteers, we assessed features such as absorbance values at wavenumbers of 1033 and 1076 cm , and the ratios at 1121 versus 1020 cm and 1629 versus 1737 cm . Through the deconvolution of the amide I band, protein secondary structure analysis was performed.

Confidence intervals for the Youden index and its optimal cut-off point in the presence of covariates.

In medical diagnostic studies, the Youden index is a summary measure widely used in the evaluation of the diagnostic accuracy of a medical test. When covariates are not considered, the diagnostic accuracy of the test can be biased or misleading. By incorporating information from covariates using linear regression models, we propose generalized confidence intervals for the covariate-adjusted Youden index and its optimal cut-off point.

Empirical likelihood-based inferences for median medical cost regression models with censored data.

Recent studies show that medical cost data can be heavily censored and highly skewed, which leads to have more complex cost data analysis. In this paper, we propose influence function and empirical likelihood (EL)-based methods to construct confidence regions for regression parameters in median cost regression models with censored data. We further propose confidence intervals for the median cost with given covariates using the proposed EL-based confidence regions.

Direct estimation of the area under the receiver operating characteristic curve with verification biased data.

In medical diagnostic studies, verification of the true disease status might be partially missing based on results of diagnostic tests and other characteristics of subjects. Because estimates of area under the ROC curve (AUC) based on partially validated subjects are usually biased, it is usually necessary to estimate AUC from a bias-corrected ROC curve. In this article, various direct estimation methods of the AUC based on hybrid imputation [full imputations and mean score imputation (MSI)], inverse probability weighting, and the semiparametric efficient (SPE) approach are proposed and compared in the presence of verification bias when the test result is continuous under the assumption that the true disease status, if missing, is missing at random.

Optimizing infrared spectral discrimination to enhance disease diagnostics: monitoring the signatures of inflammatory bowel diseases with anti-TNFα therapy.

This study presents an application of infrared spectroscopy of sera for monitoring the efficacy of anti-TNFα therapy for inflammatory bowel diseases. Understanding the therapeutic response includes the analysis of absorption bands representing constituent molecules. Interleukin-10 knockout mouse model of the diseases with anti-TNFα treatment was used.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis.

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients.

Bayesian and influence function-based empirical likelihoods for inference of sensitivity in diagnostic tests.

In medical diagnostic studies, a diagnostic test can be evaluated based on its sensitivity under a desired specificity. Existing methods for inference on sensitivity include normal approximation-based approaches and empirical likelihood (EL)-based approaches. These methods generally have poor performance when the specificity is high, and some require choosing smoothing parameters.

Diabetes retinopathy and related health management in Asians versus whites using BRFSS 2005-2017 data.

Purpose: Asian Americans had high rate of type 2 diabetes and less risk for diabetes complications compared to white. The purpose of this study was to examine diabetic retinopathy and related healthcare management among Asian American adults with diabetes.

Materials And Method: Asian and white type 2 diabetes participants from 2005-2017 Behavioral Risk Factor Surveillance System (BRFSS) data were used to perform the analysis.

Influence function-based empirical likelihood for inference of quantile medical costs with censored data.

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs.

Partial Youden index and its inferences.

In medical diagnostic research, medical tests with continuous values are widely employed to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a medical test can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and determine an optimal cut-off point for test results, the Youden index is commonly used.

Empirical likelihood-based confidence intervals for mean medical cost with censored data.

In this paper, we propose empirical likelihood methods based on influence function and jackknife techniques for constructing confidence intervals for mean medical cost with censored data. We conduct a simulation study to compare the coverage probabilities and interval lengths of our proposed confidence intervals with that of the existing normal approximation-based confidence intervals and bootstrap confidence intervals. The proposed methods have better finite-sample performances than existing methods.

On coalescence analysis using genealogy rooted trees.

DNA sequence data are now being used to study the ancestral history of human population. The existing methods for such coalescence inference use recursion formula to compute the data probabilities. These methods are useful in practical applications, but computationally complicated.

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.

-statistic with side information.

In this paper we study -statistics with side information incorporated using the method of empirical likelihood. Some basic properties of the proposed statistics are investigated. We find that by implementing the side information properly, the proposed -statistics can have smaller asymptotic variance than the existing -statistics in the literature.

Confidence intervals for the difference in paired Youden indices.

Comparison of accuracy between two diagnostic tests can be implemented by investigating the difference in paired Youden indices. However, few literature articles have discussed the inferences for the difference in paired Youden indices. In this paper, we propose an exact confidence interval for the difference in paired Youden indices based on the generalized pivotal quantities.

New nonparametric confidence intervals for the Youden index.

The Youden index, a main summary index for the receiver operating characteristic (ROC) curve, is a comprehensive measurement for the effectiveness of a diagnostic test. For a continuous-scale diagnostic test, the optimal cut point for positive disease is the cut point leading to the maximization of the sum of sensitivity and specificity. Finding the Youden index of the test is equivalent to maximize the sum of sensitivity and specificity for all the possible values of the cut point.

Optimal Combinations of Diagnostic Tests Based on AUC.

When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic.

Empirical likelihood-based confidence intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity.

For a continuous-scale diagnostic test, it is often of interest to find the range of the sensitivity of the test at the cut-off that yields a desired specificity. In this article, we first define a profile empirical likelihood ratio for the sensitivity of a continuous-scale diagnostic test and show that its limiting distribution is a scaled chi-square distribution. We then propose two new empirical likelihood-based confidence intervals for the sensitivity of the test at a fixed level of specificity by using the scaled chi-square distribution.

Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test.

The accuracy of a diagnostic test with continuous-scale results is of high importance in clinical medicine. It is often summarised by the area under the ROC curve (AUC). In this article, we discuss and compare nine non-parametric confidence intervals of the AUC for a continuous-scale diagnostic test.

Empirical likelihood inference for the area under the ROC curve.

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.

New confidence intervals for the difference between two sensitivities at a fixed level of specificity.

For two continuous-scale diagnostic tests, it is of interest to compare their sensitivities at a predetermined level of specificity. In this paper, we propose three new intervals for the difference between two sensitivities at a fixed level of specificity. These intervals are easy to compute.

Improved confidence intervals for the sensitivity at a fixed level of specificity of a continuous-scale diagnostic test.

For a continuous-scale diagnostic test, it is of interest to construct a confidence interval for the sensitivity of the diagnostic test at the cut-off that yields a predetermined level of its specificity (for example, 80, 90 or 95 per cent). In this paper we propose two new intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity. We then conduct simulation studies to compare the relative performance of these two intervals with the best existing BCa bootstrap interval, proposed by Platt et al.