Comparison of the Average Kappa Coefficients of Two Binary Diagnostic Tests with Missing Data

The average kappa coefficient of a binary diagnostic test is a parameter that measures the average beyond‐chance agreement between the diagnostic test and the gold standard. This parameter depends on the accuracy of the diagnostic test and also on the disease prevalence. This article studies the comparison of the average kappa coefficients of two binary diagnostic tests when the gold standard is not applied to all individuals in a random sample. In this situation, known as partial disease verification, the disease status of some individuals is a missing piece of data. Assuming that the missing data mechanism is missing at random, the comparison of the average kappa coefficients is solved by applying two computational methods: the EM algorithm and the SEM algorithm. With the EM algorithm the parameters are estimated and with the SEM algorithm their variances‐covariances are estimated. Simulation experiments have been carried out to study the sizes and powers of the hypothesis tests studied, obtaining that the proposed method has good asymptotic behavior. A function has been written in R to solve the proposed problem, and the results obtained have been applied to the diagnosis of Alzheimerʹs disease

Coronary Evaluation Using Multi-detector Spiral Computed Tomography Angiography: Statistical Design and Analysis

Contrast-enhanced multi-detector row spiral computed tomography (MDCT) has been introduced as a method for non-invasive visualization of coronary artery stenosis. To determine the diagnostic accuracy of MDCT coronary angiography, as compared to the “gold standard” invasive coronary angiography, sensitivity and specificity are estimated (95% Confidence Intervals). Three separate levels of estimation are computed: at the patient level, at the coronary artery level, and at the coronary artery segment level. We review the methodology for the estimation of sensitivity and specificity of non-clustered binary data (patient level analysis) and present a methodology for the estimation of sensitivity and specificity that considers the patient as a cluster and the coronary arteries (or coronary artery segments) as the diagnostic units of the study (DUOS) within each cluster. We also present how to estimate the weighted kappa for the comparison of ordinal measures of stenosis when non-clustered and clustered data are considered and the mean difference for the comparison of continuous measures of stenosis when non-clustered and clustered data are considered. Finally, we present a methods for determining the statistical precision of estimates sensitivity and specificity, weighted kappa and mean difference when clustered data are considered

A review of agreement measure as a subset of association measure between raters

Agreement can be regarded as a special case of association and not the other way round. Virtually in all life or social science researches, subjects are being classified into categories by raters, interviewers or observers and both association and agreement measures can be obtained from the results of this researchers. The distinction between association and agreement for a given data is that, for two responses to be perfectly associated we require that we can predict the category of one response from the category of the other response, while for two response to agree, they must fall into the identical category. Which hence mean, once there is agreement between the two responses, association has already exist, however, strong association may exist between the two responses without any strong agreement. Many approaches have been proposed by various authors for measuring each of these measures. In this work, we present some up till date development on these measures statistics

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles

Statistical considerations in a systematic review of proxy measures of clinical behaviour

Peer reviewedPublisher PD

Statistical tools to improve assessing agreement between several observers

In the context of assessing the impact of management and environmental factors on animal health, behaviour or performance it has become increasingly important to conduct (epidemiological) studies in the field. Hence, the number of investigated farms per study is considerably high so that numerous observers are needed for investigation. In order to maintain the quality and validity of study results calibration meetings where observers are trained and the current level of agreement is assessed have to be conducted to minimise the observer effect. When study animals were rated independently by the same observers by a categorical variable the exclusion test can be performed to identify disagreeing observers. This statistical test compares for each variable and each observer the observer-specific agreement with the overall agreement among all observers based on kappa coefficients. It accounts for two major challenges, namely the absence of a gold-standard observer and different data type comprising ordinal, nominal and binary data. The presented methods are applied on a reliability study to assess the agreement among eight observers rating welfare parameters of laying hens. The degree to which the observers agreed depended on the investigated item (global weighted kappa coefficients: 0.37 to 0.94). The proposed method and graphical description served to assess the direction and degree to which an observer deviates from the others. It is suggested to further improve studies with numerous observers by conducting calibration meetings and accounting for observer bias

Sodium and potassium disturbances in childhood diarrhoea

Includes bibliographical references