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

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.     

Two-stage models for the analysis of cancer screening data

Methods are proposed for the analysis of the natural history of disease from screening data when it cannot be assumed that untreated preclinical disease always progresses to clinical disease. The methodology is based on a two-stage model for preclinical disease in which stage 1 lesions may or may not progress to stage 2, but all stage 2 lesions progress to clinical disease. The focus is on joint estimation of the total preclinical duration and the sensitivity of the screening test. A partial likelihood is proposed for the analysis of prospectively collected screening data, and an analogous conditional likelihood is proposed for retrospective data. Some special cases for the joint sojourn distribution of the two stages are considered, including the independent model and limiting models where the duration of stage 2 is short relative to stage 1. The methods are applied to a case-control study of cervical cancer screening in Northeast Scotland.     

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work.     

On Mendelian randomization analysis of case-control study

Mendelian randomization (MR) analysis uses genotypes as instruments to estimate the causal effect of an exposure in the presence of unobserved confounders. The existing MR methods focus on the data generated from prospective cohort studies. We develop a procedure for studying binary outcomes under a case-control design. The proposed procedure is built upon two working models commonly used for MR analyses and adopts a quasi-empirical likelihood framework to address the ascertainment bias from case-control sampling. We derive various approaches for estimating the causal effect and hypothesis testing under the empirical likelihood framework. We conduct extensive simulation studies to evaluate the proposed methods. We find that the proposed empirical likelihood estimate is less biased than the existing estimates. Among all the approaches considered, the Lagrange multiplier (LM) test has the highest power, and the confidence intervals derived from the LM test have the most accurate coverage. We illustrate the use of our method in MR analysis of prostate cancer case-control data with vitamin D level as exposure and three single nucleotide polymorphisms as instruments.     

Covariate adjustment in continuous biomarker assessment

Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate-specific control of the sensitivity. Variance estimates are obtained from a sample-based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate-adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer.     

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data

Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns''s disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn''s disease; and one novel association implicating a gene involved in apoptosis pathways in rheumatoid arthritis. We provide software for applying our PUMA analysis framework.     

Space–time patterns during the establishment of a nonindigenous species

Increasing rates of global trade and travel have the invariable consequence of an increase in the likelihood of nonindigenous species arrival, and some new arrivals are successful in establishing themselves. Quantifying the pattern of establishment of nonindigenous species across both spatial and temporal scales is paramount in early detection efforts, yet very difficult to accomplish. Previous work in epidemiology has proposed methods for assessing the space–time properties of emerging infectious diseases by quantifying the degree of space–time clustering between individual cases. I tested the applicability of one such method commonly used in epidemiology, the Knox test for space–time interaction, to analyze rare abundance data from an isolated, newly-establishing gypsy moth, Lymantria dispar, population in Minnesota, USA, and incorporated a bootstrap approach to quantify the space–time pattern in a random process that can be used to compare with results from empirical data. The use of the Knox test in assessing the establishment phase of biological invasions could potentially serve as an early warning system against new invaders, particularly for those with a known history of a repeated number of arrivals.     

Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard

Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation-Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data-based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.     

Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis,Blood Analysis and Disease History

Background

Chronic kidney disease (CKD) is a disorder associated with breakdown of kidney structure and function. CKD can be diagnosed in its early stage only by experienced nephrologists and urologists (medical experts) using the disease history, symptoms and laboratory tests. There are few studies related to the automatic diagnosis of CKD in the literature. However, these methods are not adequate to help the medical experts.

Are moment bounds on the recombination fraction between a marker and a disease locus too good to be true? Allelic association mapping revisited for simple genetic diseases in the Finnish population.

In the past several years, allelic association has helped map a number of rare genetic diseases in the human genome. A commonly used upper bound on the recombination fraction between the disease gene and an associated marker is known to be biased downward, so there is the possibility that an investigator could be misled. This upper bound is based on a moment equation that can be derived within the context of a Poisson branching process, so its performance can be compared with a recently proposed likelihood bound. We show that the confidence level of the moment upper bound is much lower than expected, while the confidence level of the likelihood bound is in line with expectation. The effects of mutation at either the marker or disease locus on the upper bounds are also investigated. Results indicate that mutation is not an important force for typical mutation rates, unless the recombination fraction between the marker and disease locus is very small or the disease allele is very rare in the general population. Finally, the impact of sample size on the likelihood bound is investigated. The results are illustrated with data on 10 simple genetic diseased in the Finnish population.     

Likelihood Ratio Test for Excess Homozygosity at Marker Loci on X Chromosome

The assumption of Hardy-Weinberg equilibrium (HWE) is generally required for association analysis using case-control design on autosomes; otherwise, the size may be inflated. There has been an increasing interest of exploring the association between diseases and markers on X chromosome and the effect of the departure from HWE on association analysis on X chromosome. Note that there are two hypotheses of interest regarding the X chromosome: (i) the frequencies of the same allele at a locus in males and females are equal and (ii) the inbreeding coefficient in females is zero (without excess homozygosity). Thus, excess homozygosity and significantly different minor allele frequencies between males and females are used to filter X-linked variants. There are two existing methods to test for (i) and (ii), respectively. However, their size and powers have not been studied yet. Further, there is no existing method to simultaneously detect both hypotheses till now. Therefore, in this article, we propose a novel likelihood ratio test for both (i) and (ii) on X chromosome. To further investigate the underlying reason why the null hypothesis is statistically rejected, we also develop two likelihood ratio tests for detecting (i) and (ii), respectively. Moreover, we explore the effect of population stratification on the proposed tests. From our simulation study, the size of the test for (i) is close to the nominal significance level. However, the size of the excess homozygosity test and the test for both (i) and (ii) is conservative. So, we propose parametric bootstrap techniques to evaluate their validity and performance. Simulation results show that the proposed methods with bootstrap techniques control the size well under the respective null hypothesis. Power comparison demonstrates that the methods with bootstrap techniques are more powerful than those without bootstrap procedure and the existing methods. The application of the proposed methods to a rheumatoid arthritis dataset indicates their utility.     

Role of kidney biomarkers of chronic kidney disease: An update

Chronic kidney disease (CKD) is a progressive pathological condition marked by deteriorating renal function over time. Diagnostic of kidney disease depend on serum creatinine level and glomerular filtration rate which is detectable when kidney function become half. The detection of kidney damage in an early stage needs robust biomarkers. Biomarkers allow monitoring the disease progression at initial stages of disease. On the onset of impairment in cellular organization there is perturbation in signaling molecules which are either up-regulated or down-regulated and act as an indicator or biomarker of diseased stage. This review compiled the cell signaling of different kidney biomarkers associated with the onset of chronic kidney diseases. Delay in diagnosis of CKD will cause deterioration of nephron function which leads to End stage renal disease and at that point patients require dialysis or kidney transplant. Detailed information on the complex network in signaling pathway leading to a coordinated pattern of gene expression and regulation in CKD will undoubtedly provide important clues to develop novel prognostic and therapeutic strategies for CKD.     

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Toward this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease‐related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability‐index model to link multiple genetically regulated expressions from PrediXcan and the manifest variables including endophenotypes and case‐control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database.     

Prediction-based structured variable selection through the receiver operating characteristic curves

In many clinical settings, a commonly encountered problem is to assess accuracy of a screening test for early detection of a disease. In these applications, predictive performance of the test is of interest. Variable selection may be useful in designing a medical test. An example is a research study conducted to design a new screening test by selecting variables from an existing screener with a hierarchical structure among variables: there are several root questions followed by their stem questions. The stem questions will only be asked after a subject has answered the root question. It is therefore unreasonable to select a model that only contains stem variables but not its root variable. In this work, we propose methods to perform variable selection with structured variables when predictive accuracy of a diagnostic test is the main concern of the analysis. We take a linear combination of individual variables to form a combined test. We then maximize a direct summary measure of the predictive performance of the test, the area under a receiver operating characteristic curve (AUC of an ROC), subject to a penalty function to control for overfitting. Since maximizing empirical AUC of the ROC of a combined test is a complicated nonconvex problem (Pepe, Cai, and Longton, 2006, Biometrics62, 221-229), we explore the connection between the empirical AUC and a support vector machine (SVM). We cast the problem of maximizing predictive performance of a combined test as a penalized SVM problem and apply a reparametrization to impose the hierarchical structure among variables. We also describe a penalized logistic regression variable selection procedure for structured variables and compare it with the ROC-based approaches. We use simulation studies based on real data to examine performance of the proposed methods. Finally we apply developed methods to design a structured screener to be used in primary care clinics to refer potentially psychotic patients for further specialty diagnostics and treatment.     

Bootstrap Restricted Likelihood Ratio Test for the Detection of Rare Variants

In this paper the detection of rare variants association with continuous phenotypes of interest is investigated via the likelihood-ratio based variance component test under the framework of linear mixed models. The hypothesis testing is challenging and nonstandard, since under the null the variance component is located on the boundary of its parameter space. In this situation the usual asymptotic chisquare distribution of the likelihood ratio statistic does not necessarily hold. To circumvent the derivation of the null distribution we resort to the bootstrap method due to its generic applicability and being easy to implement. Both parametric and nonparametric bootstrap likelihood ratio tests are studied. Numerical studies are implemented to evaluate the performance of the proposed bootstrap likelihood ratio test and compare to some existing methods for the identification of rare variants. To reduce the computational time of the bootstrap likelihood ratio test we propose an effective approximation mixture for the bootstrap null distribution. The GAW17 data is used to illustrate the proposed test.     

Statistical Inference For Risk Difference in an Incomplete Correlated 2 × 2 Table

In some infectious disease studies and 2‐step treatment studies, 2 × 2 table with structural zero could arise in situations where it is theoretically impossible for a particular cell to contain observations or structural void is introduced by design. In this article, we propose a score test of hypotheses pertaining to the marginal and conditional probabilities in a 2 × 2 table with structural zero via the risk/rate difference measure. Score test‐based confidence interval will also be outlined. We evaluate the performance of the score test and the existing likelihood ratio test. Our empirical results evince the similar and satisfactory performance of the two tests (with appropriate adjustments) in terms of coverage probability and expected interval width. Both tests consistently perform well from small‐ to moderate‐sample designs. The score test however has the advantage that it is only undefined in one scenario while the likelihood ratio test can be undefined in many scenarios. We illustrate our method by a real example from a two‐step tuberculosis skin test study.     

Thyroid-Stimulating Antibody/Thyroid-Stimulating Hormone Receptor Antibody Ratio as a Sensitive Screening Test for Active Graves’ Orbitopathy

ObjectiveGraves’ orbitopathy (GO), an extrathyroidal manifestation of Graves’ disease, can seriously threaten a patient's quality of life. Given that immunosuppressive treatment during the early active phase of GO has been found to reduce both disease activity and severity, sensitive screening tests are needed.MethodsThe present study included 86 patients with GO, in whom serum levels of thyroid-stimulating hormone (TSH), free triiodothyronine (T3), free thyroxine, thyroid-stimulating antibody, TSH receptor antibody, thyroid peroxidase antibody, thyroglobulin, and thyroglobulin antibody were measured within 2 months before magnetic resonance imaging (MRI) for orbit assessment.ResultsThe thyroid-stimulating antibody/TSH receptor antibody ratio was able to distinguish MRI results with a correct classification rate of 81%. When focusing on patients without T3 predominant Graves’ diseases, the ratio distinguished MRI results at a rate of 92%. Receiver operating characteristic curve analysis revealed a cutoff antibody ratio of 87, which yielded a sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 91%, 95%, 18.2, and 0.0957, respectively, for distinguished MRI results.ConclusionsThe thyroid-stimulating antibody/TSH receptor antibody ratio is a highly sensitive and specific indicator for active GO, especially in patients without T3 predominance, and serves as a good screening test for active GO in primary care settings.     

A Model for Adjusting for Nonignorable Verification Bias in Estimation of the ROC Curve and Its Area with Likelihood‐Based Approach

Summary In estimation of the ROC curve, when the true disease status is subject to nonignorable missingness, the observed likelihood involves the missing mechanism given by a selection model. In this article, we proposed a likelihood‐based approach to estimate the ROC curve and the area under the ROC curve when the verification bias is nonignorable. We specified a parametric disease model in order to make the nonignorable selection model identifiable. With the estimated verification and disease probabilities, we constructed four types of empirical estimates of the ROC curve and its area based on imputation and reweighting methods. In practice, a reasonably large sample size is required to estimate the nonignorable selection model in our settings. Simulation studies showed that all four estimators of ROC area performed well, and imputation estimators were generally more efficient than the other estimators proposed. We applied the proposed method to a data set from research in Alzheimer's disease.     

Behavioral tests as indicator for pain and distress in a primate endometriosis model

Background As common marmosets (Callithrix jacchus) are frequently used experimental animals, sensitive test systems are needed to evaluate impairment and pain caused by procedures and diseases. Methods A diurnal profile of healthy animals was obtained by videotaping. Differences in social behavior and cognitive skills between marmosets with established endometriosis and healthy monkeys were investigated using the videotaping, the Wisconsin General Test Apparatus (WGTA), and a food tree. Results The marmosets showed a mostly trimodal course of activity. Social grooming and activity were significantly decreased in animals with endometriosis; furthermore, the diseased monkeys habituated significantly worse to the cognitive test settings. The food tree experiments offered no differences between diseased and control animals. Conclusion The videotaping and the WGTA are suitable methods to detect disease‐related impairments in common marmosets, which is essential for the refinement of experiments.