Objectives: The objective of the study was to investigate the application and interpretation of statistical analyses in a cross-section of systematic reviews (SRs) of therapeutic interventions, without restriction by journal, clinical condition, or specialty.
Study Design And Setting: We evaluated a random sample of SRs assembled previously, which were indexed in MEDLINE® during February 2014, focused on a treatment or prevention question, and reported at least one meta-analysis. The reported statistical methods used in each SR were extracted from articles and online appendices by one author, with a 20% random sample extracted in duplicate.
Results: We evaluated 110 SRs; 78/110 (71%) were non-Cochrane SRs and 55/110 (50%) investigated a pharmacological intervention. The SRs presented a median of 13 (interquartile range: 5-27) meta-analytic effects. When considering the index (primary or first reported) meta-analysis of each SR, just over half (62/110 [56%]) used the random-effects model, but few (5/62 [8%]) interpreted the meta-analytic effect correctly (as the average of the intervention effects across all studies). A statistical test for funnel plot asymmetry was reported in 17/110 (15%) SRs; however, in only 4/17 (24%) did the test include the recommended number of at least 10 studies of varying size. Subgroup analyses accompanied 42/110 (38%) index meta-analyses, but findings were not interpreted with respect to a test for interaction in 29/42 (69%) cases, and the issue of potential confounding in the subgroup analyses was not raised in any SR.
Conclusions: There is scope for improvement in the application and interpretation of statistical analyses in SRs of therapeutic interventions. The involvement of statisticians on the SR team and establishment of partnerships between researchers with specialist expertise in SR methods and journal editors may help overcome these shortcomings.
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http://dx.doi.org/10.1016/j.jclinepi.2017.11.022 | DOI Listing |
Nature
January 2025
German Centre for Cardiovascular Research (DZHK), Partner Site Lower Saxony, Göttingen, Germany.
Cardiomyocytes can be implanted to remuscularize the failing heart. Challenges include sufficient cardiomyocyte retention for a sustainable therapeutic impact without intolerable side effects, such as arrhythmia and tumour growth. We investigated the hypothesis that epicardial engineered heart muscle (EHM) allografts from induced pluripotent stem cell-derived cardiomyocytes and stromal cells structurally and functionally remuscularize the chronically failing heart without limiting side effects in rhesus macaques.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh.
The transportation industry contributes significantly to climate change through carbon dioxide ( ) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government's official open data portal, we explored the impact of various vehicle attributes on emissions.
View Article and Find Full Text PDFAnal Chim Acta
March 2025
Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan. Electronic address:
Background: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported.
View Article and Find Full Text PDFObjectives: This study aimed to develop a prediction model for the detection of early sepsis-associated acute kidney injury (SA-AKI), which is defined as AKI diagnosed within 48 hours of a sepsis diagnosis.
Design: A retrospective study design was employed. It is not linked to a clinical trial.
Bioinformatics
January 2025
Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
Motivation: Numerous microbiome studies have revealed significant associations between the microbiome and human health and disease. These findings have motivated researchers to explore the causal role of the microbiome in human complex traits and diseases. However, the complexities of microbiome data pose challenges for statistical analysis and interpretation of causal effects.
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