In a matched-pair study, when outcomes of two diagnostic tests are ordinal/continuous, the difference between two correlated areas under ROC curves (AUCs) is usually used to compare the overall discriminatory ability of two diagnostic tests. This article considers confidence interval (CI) construction problems of difference between two correlated AUCs in a matched-pair experiment, and proposes 13 hybrid CIs based on variance estimates recovery with the maximum likelihood estimation, Delong's statistic, Wilson score statistic (WS) and WS with continuity correction, the modified Wald statistic (MW) and MW with continuity correction and Agresti-Coull statistic, and three Bootstrap-resampling-based CIs. For comparison, we present traditional parametric and nonparametric CIs. Simulation studies are conducted to assess the performance of the proposed CIs in terms of empirical coverage probabilities, empirical interval widths, and ratios of the mesial noncoverage probabilities to the noncoverage probabilities. Two examples from clinical studies are illustrated by the proposed methodologies. Empirical results evidence that the hybrid Agresti-Coull CI with the empirical estimation (EAC) behaved most satisfactorily because its coverage probability was quite close to the prespecified confidence level with short interval width. Hence, we recommend the usage of the EAC CI in applications.
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With the popularity of posting memes on social platforms, the severe negative impact of hateful memes is growing. As existing detection models have lower detection accuracy than humans, hateful memes detection is still a challenge to statistical learning and artificial intelligence. This paper proposed a multi-task learning method consisting of a primary multimodal task and two unimodal auxiliary tasks to address this issue. We introduced a self-supervised generation strategy in auxiliary tasks to generate unimodal auxiliary labels automatically. Meanwhile, we used BERT and RESNET as the backbone for text and image classification, respectively, and then fusion them with a late fusion method. In the training phase, the backward guidance technique and the adaptive weight adjustment strategy were used to capture the consistency and variability between different modalities, numerically improving the hateful memes detection accuracy and the generalization and robustness of the model. The experiment conducted on the Facebook AI multimodal hateful memes dataset shows that the prediction accuracy of our model outperformed the comparing models.
A three-arm non-inferiority trial including a placebo is usually utilized to assess the non-inferiority of an experimental treatment to a reference treatment. Existing methods for assessing non-inferiority mainly focus on the fully observed endpoints. However, in some clinical trials, treatment endpoints may be subject to missingness for various reasons, such as the refusal of subjects or their migration. To address this issue, this paper aims to develop a non-parametric approach to assess the non-inferiority of an experimental treatment to a reference treatment in a three-arm trial with non-ignorable missing endpoints. A logistic regression is adopted to specify a non-ignorable missingness data mechanism. A semi-parametric imputation method is proposed to estimate parameters in the considered logistic regression. Inverse probability weighting, augmented inverse probability weighting and non-parametric methods are developed to estimate treatment efficacy for known and unknown parameters in the considered logistic regression. Under some regularity conditions, we show asymptotic normality of the constructed estimators for treatment efficacy. A bootstrap resampling method is presented to estimate asymptotic variances of the estimated treatment efficacy. Three Wald-type statistics are constructed to test the non-inferiority based on the asymptotic properties of the estimated treatment efficacy. Empirical studies show that the proposed Wald-type test procedure is robust to the misspecified missingness data mechanism, and behaves better than the complete-case method in the sense that the type I error rates for the former are closer to the pre-given significance level than those for the latter.
Monitoring of soil nitrogen (N) cycling is useful to assess soil quality and to gauge the sustainability of management practices. We studied net N mineralization, nitrification, and soil N availability in the 0−10 cm and 11−30 cm soil horizons in east China during 2006−2007 using an in situ incubation method in four subtropical evergreen broad-leaved forest stands aged 18-, 36-, 48-, and 65-years. The properties of surface soil and forest floor varied between stand age classes. C:N ratios of surface soil and forest floor decreased, whereas soil total N and total organic C, available P, and soil microbial biomass N increased with stand age. The mineral N pool was small for the young stand and large for the older stands. NO 3 --N was less than 30% in all stands. Net rates of N mineralization and nitrification were higher in old stands than in younger stands, and higher in the 0−10 cm than in the 11−30 cm horizon.The differences were significant between old and young stands (p < 0.031) and between soil horizons (p < 0.005). Relative nitrification was somewhat low in all forest stands and declined with stand age. N transformation seemed to be controlled by soil moisture, soil microbial biomass N, and forest floor C:N ratio. Our results demonstrate that analyses of N cycling can provide insight into the effects of management disturbances on forest ecosystems.
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