We consider the problem of drawing superiority inferences on individual endpoints following non-inferiority testing. Röhmel et al. (2006) pointed out this as an important problem which had not been addressed by the previous procedures that only tested for global superiority. Röhmel et al. objected to incorporating the non-inferiority tests in the assessment of the global superiority test by exploiting the relationship between the two, since the results of the latter test then depend on the non-inferiority margins specified for the former test. We argue that this is justified, besides the fact that it enhances the power of the global superiority test. We provide a closed testing formulation which generalizes the three-step procedure proposed by Röhmel et al. for two endpoints. For the global superiority test, Röhmel et al. suggest using the Läuter (1996) test which is modified to make it monotone. The resulting test not only is complicated to use, but the modification does not readily extend to more than two endpoints, and it is less powerful in general than several of its competitors. This is verified in a simulation study. Instead, we suggest applying the one-sided likelihood ratio test used by Perlman and Wu (2004) or the union-intersection t(max) test used by Tamhane and Logan (2004).
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http://dx.doi.org/10.1002/bimj.200710447 | DOI Listing |
Cancer Med
February 2025
Pulmonology and Thoracic Oncology Department, APHP Hôpital Tenon and Sorbonne Université, Paris, France.
Background: Real-world data regarding patients with non-small cell lung cancer (NSCLC) with EGFR exon 20 insertion (ex20ins) mutations receiving mobocertinib are limited. This study describes these patients' characteristics and outcomes.
Methods: A chart review was conducted across three countries (Canada, France, and Hong Kong), abstracting data from eligible patients (NCT05207423).
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Saudi Arabia.
Fructose-driven metabolic disorders, such as obesity, non-alcoholic fatty liver disease (NAFLD), dyslipidemia, and type 2 diabetes, are significant global health challenges. Ketohexokinase C (KHK-C), a key enzyme in fructose metabolism, is a promising therapeutic target. α-Mangostin, a naturally occurring prenylated xanthone, has been identified as an effective KHK-C inhibitor, prompting exploration of its analogs for enhanced efficacy.
View Article and Find Full Text PDFPathogens
January 2025
National Reference Laboratory (NRL) for Swine Fever, Istituto Zooprofilattico Sperimentale dell' Umbria e delle Marche "Togo Rosati", 06126 Perugia, Italy.
African swine fever (ASF), characterized by high mortality rates in infected animals, remains a significant global veterinary and economic concern, due to the widespread distribution of ASF virus (ASFV) genotype II across five continents. In this study, ASFV strains collected in Italy during 2022-2023 from two geographical clusters, North-West (Alessandria) and Calabria, were fully sequenced. In addition, an in vivo experiment in pigs was performed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Forest Biometrics and Remote Sensing Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA.
Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species.
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