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. Statistical evaluation of the test performance based only on test results from subjects with verified disease status are typically biased. Various AUC estimation methods for tests with verification biased data have been developed over the last few decades. In this article, we develop new direct estimation methods for the volume under the ROC surface (VUS) by extending the AUC estimation methods for two-class diagnostic tests to three-class diagnostic tests in the presence of verification bias. The proposed methods will provide a comprehensive guide to deal with the verification bias in three-class diagnostic test accuracy studies and lead to a better choice of diagnostic tests.

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. Numerical studies are performed to compare the finite sample performances of the proposed approaches with existing methods. The proposed methods are shown to outperform existing methods in terms of coverage probability. A real dataset from the Alzheimer's Disease Neuroimaging Initiative (ANDI) is used to illustrate the proposed methods.

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-1 , and the ratios at 1121 versus 1020 cm-1 and 1629 versus 1737 cm-1 . Through the deconvolution of the amide I band, protein secondary structure analysis was performed. Colitis-induced alterations are reflected as fluctuations in the vibrational modes, and are used to identify associated spectral signatures. The results of the study show statistically significant differences in five identifying spectral signatures. Among them, the sensitivity and specificity of the spectral signature, I1121 /I1020 , were 100% and 86%, respectively. These findings resemble our earlier proof-of-concept investigations in mouse models and provide preliminary evidence that this could be a reliable diagnostic test for human patients.

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. Simulation studies are conducted to compare the proposed EL-based confidence regions with the existing normal approximation-based confidence regions in terms of coverage probabilities. The new EL-based methods are observed to have better finite sample performances than existing methods particularly when the censoring proportion is high. The new methods are also illustrated through a real data example.

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. Furthermore, under heteroscedastic regression models, we propose various confidence intervals for the covariate-adjusted Youden index and its optimal cut-off point. Extensive simulation studies are conducted to evaluate the finite sample performance of various confidence intervals for the Youden index and its optimal cut-off point in the presence of covariates. To illustrate the application of our recommended methods, we apply the methods to a dataset on postprandial blood glucose measurements.

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. Simulation results show that the proposed estimators are accurate for the biased sampling if the disease and verification models are correctly specified. The SPE and MSI based estimators perform well even under the misspecified disease/verification models. Numerical studies are performed to compare the finite sample performance of the proposed approaches with existing methods. A real dataset of neonatal hearing screening study is analyzed.

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. The discrimination potential is optimized by analyzing data with curve fitting. It shows; antibody therapy markedly ameliorated the disease, concurring with earlier mucosal immunology and pathophysiologic studies. This technique may thus also be useful for the evaluation of mucosal healing or other therapeutic modalities of gastrointestinal tract diseases keeping the endoscopic tests as confirmatory.

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. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and ß-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

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. We propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to overcome such problems. Numerical studies are performed to compare the finite sample performance of the proposed approaches with existing methods. The proposed methods are shown to perform better in terms of both coverage probability and interval length. A real data set from Alzheimer's Disease Neuroimaging Initiative (ANDI) is analyzed.

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. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example.

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. SAS 9.4 survey procedures were used to conduct the statistical test. Health care management variables (self-blood sugar check, eye check and HbA1C check with doctors, health care professional visit) were analyzed and compared between Asian and white. RESULTS: During 2005-2017, diabetic retinopathy (DR) rate among Asian Americans was 10% higher than white, and Asian Americans was more than 100% more likely to develop DR compared to white. Asian Americans was less likely to check their blood sugar once a day (P<0.05 for all years except 2005 and 2007) and more likely to see the health care professional and perform eye and HbA1C check even the relationship was not statistically significant. After adjusting all the demo-social factors and health care management factors, Asian still had higher rate of DR compared to white. CONCLUSION: Asian Americans had higher rate of DR rate compared to white. Asian and white all had low rate of selfcare of blood sugar. Interventions for DR need to apply among Asian population.

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. In particular, the Youden index is optimized over the entire range of values for sensitivity and specificity, which determine the ROC space. However in clinical practice, one may only be interested in the regions of the ROC curve that correspond to low false-positive rates or/and high sensitivities. In this paper, a new summary index for the ROC curve, called the "partial Youden index", is defined on regions of the ROC space in which sensitivity/specificity values are of practical interest. The traditional Youden index is a special case of the partial Youden index. Various parametric and non-parametric interval estimation methods are proposed for the partial Youden index. Extensive simulation studies are conducted to evaluate the finite sample performances of the proposed methods. A real example is used to illustrate the application of the new methods.

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. Finally, we illustrate our proposed methods with a relevant example.

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. The proposed methods can be easily implemented to construct confidence regions for the evaluation of continuous-scale diagnostic tests with verification bias. Simulation studies are conducted to evaluate the finite sample performance and robustness of the proposed jackknife empirical likelihood-based confidence regions in terms of coverage probabilities. Finally, a real case analysis is provided to illustrate the application of new 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. Here we first investigate the asymptotic behavior of such inference; results indicate that, broadly, the estimated coalescent time will be consistent to a finite limit. Then we study a relatively simple computation method for this analysis and illustrate how to use it.

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. For comparison, the maximum likelihood estimate-based interval and a bootstrap-based interval are also included in the study for the difference in paired Youden indices. Abundant simulation studies are conducted to compare the relative performance of these intervals by evaluating the coverage probability and average interval length. Our simulation results demonstrate that the exact confidence interval outperforms the other two intervals even with small sample size when the underlying distributions are normal. A real application is also used to illustrate the proposed intervals.

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. In this paper, we propose new nonparametric confidence intervals for the Youden index. Extensive simulation studies are conducted to compare the relative performance of the new intervals with the existing parametric interval for the index. Our simulation results indicate that the newly developed nonparametric intervals are competitive with the existing parametric interval when the underlying distributions are correctly specified, and they outperform the existing parametric interval when the underlying distributions are misspecified. The new intervals are robust and easy to implement in practice. A real example is also used to illustrate the application of the proposed intervals.

U-statistic with side information.

In this paper we study U-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 U-statistics can have smaller asymptotic variance than the existing U-statistics in the literature. The proposed U-statistics can achieve asymptotic efficiency in a formal sense and their weak limits admit a convolution result. We also find that the corresponding U-likelihood ratio procedure, as well as the U-empirical likelihood based confidence interval construction, do not benefit from incorporating side information, a result that is consistent with the result under the standard empirical likelihood ratio procedure. The impact of incorrect side information implementation in the proposed U-statistics is also explored. Simulation studies are conducted to assess the finite sample performance of the proposed method. The numerical results show that with side information implemented, the deduction of asymptotic variance can be substantial in some cases, and the coverage probability of the confidence interval using the U-empirical likelihood ratio based method outperforms that of the normal approximation based method, in particular in the cases when the underlying distribution is skewed.

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. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples.