Global Hypothesis Test to Compare the Predictive Values of Diagnostic Tests Subject to a Case-Control Design

Use of a case-control design to compare the accuracy of two binary diagnostic tests is frequent in clinical practice. This design consists of applying the two diagnostic tests to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. This manuscript studies the comparison of the predictive values of two diagnostic tests subject to a case-control design. A global hypothesis test, based on the chi-square distribution, is proposed to compare the predictive values simultaneously, as well as other alternative methods. The hypothesis tests studied require knowing the prevalence of the disease. Simulation experiments were carried out to study the type I errors and the powers of the hypothesis tests proposed, as well as to study the effect of a misspecification of the prevalence on the asymptotic behavior of the hypothesis tests and on the estimators of the predictive values. The proposed global hypothesis test was extended to the situation in which there are more than two diagnostic tests. The results have been applied to the diagnosis of coronary disease.Spanish Ministry of Economy, Grant Number MTM2016- 76938-PUniversity of Nouakchott Alaasriy

Compbdt: an R program to compare two binary diagnostic tests subject to a paired design

I thank the Editor and the two referees for their helpful comments that improved the quality of the manuscript.Background: The comparison of the performance of two binary diagnostic tests is an important topic in Clinical Medicine. The most frequent type of sample design to compare two binary diagnostic tests is the paired design. This design consists of applying the two binary diagnostic tests to all of the individuals in a random sample, where the disease status of each individual is known through the application of a gold standard. This article presents an R program to compare parameters of two binary tests subject to a paired design. Results: The “compbdt” program estimates the sensitivity and the specificity, the likelihood ratios and the predictive values of each diagnostic test applying the confidence intervals with the best asymptotic performance. The program compares the sensitivities and specificities of the two diagnostic tests simultaneously, as well as the likelihood ratios and the predictive values, applying the global hypothesis tests with the best performance in terms of type I error and power. When the global hypothesis test is significant, the causes of the significance are investigated solving the individual hypothesis tests and applying the multiple comparison method of Holm. The most optimal confidence intervals are also calculated for the difference or ratio between the respective parameters. Based on the data observed in the sample, the program also estimates the probability of making a type II error if the null hypothesis is not rejected, or estimates the power if the if the alternative hypothesis is accepted. The “compbdt” program provides all the necessary results so that the researcher can easily interpret them. The estimation of the probability of making a type II error allows the researcher to decide about the reliability of the null hypothesis when this hypothesis is not rejected. The “compbdt” program has been applied to a real example on the diagnosis of coronary artery disease. Conclusions: The “compbdt” program is one which is easy to use and allows the researcher to compare the most important parameters of two binary tests subject to a paired design. The “compbdt” program is available as supplementary material.This research was supported by the Spanish Ministry of Economy, Grant Number MTM2016–76938-P

Added predictive value of high-throughput molecular data to clinical data, and its validation

Hundreds of ''molecular signatures'' have been proposed in the literature to predict patient outcome in clinical settings from high-dimensional data, many of which eventually failed to get validated. Validation of such molecular research findings is thus becoming an increasingly important branch of clinical bioinformatics. Moreover, in practice well-known clinical predictors are often already available. From a statistical and bioinformatics point of view, poor attention has been given to the evaluation of the added predictive value of a molecular signature given that clinical predictors are available. This article reviews procedures that assess and validate the added predictive value of high-dimensional molecular data. It critically surveys various approaches for the construction of combined prediction models using both clinical and molecular data, for validating added predictive value based on independent data, and for assessing added predictive value using a single data set

Comparison of Two or More Correlated AUCs in Paired Sample Design

Purpose of study Methods of comparing the accuracy of diagnostic tests are of increasing necessity in biomedical science. When a test result is measured on a continuous scale, an assessment of the performance of the overall value of the test can be made using the Receiver Operating Characteristic (ROC) curve. This curve describes the discrimination ability of a diagnosis test in terms of diseased subjects from non-diseased subjects. The area under the ROC curve (AUC) describes the probability that a randomly chosen diseased subject will have higher probability of having disease than a randomly chosen non-diseased subject. For comparing two or more diagnostic test results, the difference between AUCs is often used. This paper proposes a non-parametric alternative method of comparing two or more correlated area under the curve (AUCs) of diagnostic tests for paired sample data. This method is based on Chi-square test statistic. Methods This paper investigated both parametric and non-parametric methods of comparing the equality of two AUCs and proposed a Chi-square test for the comparison of two or more diagnostic test processes. The proposed method does not require the knowledge of true status of subjects or gold standard in evaluating the accuracy of tests unlike other existing methods. The proposed method is most suitable for paired sample design. It also offers reliable statistical inferences even in small sample problems and circumvent the difficulties of deriving the statistical moments of complex summary statistics as seen in the Delong method. The proposed method provides for further analysis to determine the possible reason for rejecting the null hypothesis of equality of AUCs. Results The proposed method when applied on real data, avoids the lengthy and more difficult procedures of estimating the variances of two AUCs as a way of determining if two AUCs differ significantly. The method is validated using the Cochran Q test and was shown to compare favourably. The proposed method recommended for comparing two or more correlated AUCs when the data is paired. It is simple and does not require prior knowledge of true status of subjects unlike other existing methods. Keywords: Chi-square test, Cochran Q test, cut-off value, area under the curve, receiver operating characteristic, Dichotomous data DOI: 10.7176/JNSR/9-9-06 Publication date:May 31st 201

Confidence Intervals and Sample Size to Compare the Predictive Values of Two Diagnostic Tests

A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease, and a negative result indicates the absence. Positive and negative predictive values represent the accuracy of a binary diagnostic test when it is applied to a cohort of individuals, and they are measures of the clinical accuracy of the binary diagnostic test. In this manuscript, we study the comparison of the positive (negative) predictive values of two binary diagnostic tests subject to a paired design through confidence intervals. We have studied confidence intervals for the difference and for the ratio of the two positive (negative) predictive values. Simulation experiments have been carried out to study the asymptotic behavior of the confidence intervals, giving some general rules for application. We also study a method to calculate the sample size to compare the parameters using confidence intervals. We have written a program in R to solve the problems studied in this manuscript. The results have been applied to the diagnosis of colorectal cancer

Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

Generalization of Kullback-Leibler Divergence for Multi-Stage Diseases: Application to Diagnostic Test Accuracy and Optimal Cut-Points Selection Criterion

The Kullback-Leibler divergence (KL), which captures the disparity between two distributions, has been considered as a measure for determining the diagnostic performance of an ordinal diagnostic test. This study applies KL and further generalizes it to comprehensively measure the diagnostic accuracy test for multi-stage (K \u3e 2) diseases, named generalized total Kullback-Leibler divergence (GTKL). Also, GTKL is proposed as an optimal cut-points selection criterion for discriminating subjects among different disease stages. Moreover, the study investigates a variety of applications of GTKL on measuring the rule-in/out potentials in the single-stage and multi-stage levels. Intensive simulation studies are conducted to compare the performance of GTKL and other diagnostic accuracy measures, such as generalized Youden index (GYI), hypervolume under the manifold (HUM), and maximum absolute determinant (MADET). Furthermore, a comprehensive analysis of a real dataset is performed to illustrate the application of the proposed measure

Globalization of Corporate Covernance: The American Influence on Dismissal Performance Sensitivity of European CEOs

This study examines how globalization of corporate governance practices influence the risk of European CEOs being dismissed. We argue that the harsh monitoring of the American corporate governance system spills over to the rest of the world as a result of this globalization. We focus on direct and indirect American influence on the dismissal performance sensitivity among the 250 largest European publicly listed firms. The indirect influence is assumed to materialize via European firms cross-listing on U.S. exchanges, whereas the direct influence is assumed to appear as a result of European firms hiring of American independent board members. Both sources of influence are hypothesized to result in increased dismissal performance sensitivity. The empirical results show a significant increase in the dismissal sensitivity in poorly performing companies with American board membership whereas no significant increase is found from cross-listing in the U.S.CEO dismissal; Performance sensitivity; Globalization; Corporate governance; Foreign board membership; Institutional contagion