A non-repeated item (NRI) design refers to an experimental design in which items used in one level of experimental conditions are not repeatedly used at other levels. Recent literature has suggested the use of generalized linear mixed-effects models (GLMMs) for experimental data analysis, but the existing specification of GLMMs does not account for all possible dependencies among the outcomes in NRI designs. Therefore, the current study proposed a GLMM with a level-specific item random effect for NRI designs. The hypothesis testing performance of the newly proposed model was evaluated via a simulation study to detect the experimental condition effect. The model with a level-specific item random effect performed better than the existing model in terms of power when the variance of the item effect was heterogeneous. Based on these results, we suggest that experimental researchers using NRI designs consider setting a level-specific item random effect in the model.
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http://dx.doi.org/10.3389/fpsyg.2022.955722 | DOI Listing |
Sci Rep
January 2025
Space Science Centre (ANGKASA), Universiti Kebangsaan Malaysia, Bangi, 43600 UKM, Selangor D.E, Malaysia.
It is important in the rising demands to have efficient anomaly detection in camera surveillance systems for improving public safety in a complex environment. Most of the available methods usually fail to capture the long-term temporal dependencies and spatial correlations, especially in dynamic multi-camera settings. Also, many traditional methods rely heavily on large labeled datasets, generalizing poorly when encountering unseen anomalies in the process.
View Article and Find Full Text PDFAnn Hematol
January 2025
Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610042, China.
Advanced-stage extranodal natural killer/T-cell lymphoma (ENKTL) is a highly heterogeneous disease with very poor prognosis. All commonly utilized prognostic models incorporated both early-stage and advanced-stage patients in the modeling process. This study aim to design a prognostic model specifically for advanced-stage ENKTL, providing risk stratification in affected patients.
View Article and Find Full Text PDFWorld Neurosurg
January 2025
Department of Neurology, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, China. Electronic address:
Objective: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning algorithms.
Methods: Employing a retrospective cohort study design, this study collected patients with ruptured IA who received endovascular treatment at Jingzhou First People's Hospital during the inclusion period from September 2022 to December 2023. The entire dataset was randomly split into training and testing dataset with a 7:3 ratio.
Health Inf Sci Syst
December 2025
Department of Cardiac, Thoracic and Vascular Surgery, National University Hospital, Singapore, Singapore.
Sci Rep
January 2025
Physics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively.
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