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Céline Chapelle; Gwénaël Le Teuff; Paul Jacques Zufferey; Silvy Laporte; Edouard Ollier – Research Synthesis Methods, 2024
The number of meta-analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy-to-use, and specialised statistical software. Even when meta-analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the…
Descriptors: Meta Analysis, Replication (Evaluation), Data Analysis, Statistical Analysis
Maxi Schulz; Malte Kramer; Oliver Kuss; Tim Mathes – Research Synthesis Methods, 2024
In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…
Descriptors: Meta Analysis, Data Analysis, Research Methodology, Research Problems
Lee, Jihyun; Beretvas, S. Natasha – Research Synthesis Methods, 2023
Meta-analysts often encounter missing covariate values when estimating meta-regression models. In practice, ad hoc approaches involving data deletion have been widely used. The current study investigates the performance of different methods for handling missing covariates in meta-regression, including complete-case analysis (CCA), shifting-case…
Descriptors: Comparative Analysis, Research Methodology, Regression (Statistics), Meta Analysis
Lennert J. Groot; Kees-Jan Kan; Suzanne Jak – Research Synthesis Methods, 2024
Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that…
Descriptors: Meta Analysis, Structural Equation Models, Research Methodology, Data Analysis
Blaizot, Aymeric; Veettil, Sajesh K.; Saidoung, Pantakarn; Moreno-Garcia, Carlos Francisco; Wiratunga, Nirmalie; Aceves-Martins, Magaly; Lai, Nai Ming; Chaiyakunapruk, Nathorn – Research Synthesis Methods, 2022
The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases…
Descriptors: Artificial Intelligence, Literature Reviews, Databases, Data Analysis
Annabel L. Davies; A. E. Ades; Julian P. T. Higgins – Research Synthesis Methods, 2024
Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single…
Descriptors: Children, Body Composition, Measurement Techniques, Sampling
Patel, Amit; Cooper, Nicola; Freeman, Suzanne; Sutton, Alex – Research Synthesis Methods, 2021
Diagnostic test accuracy (DTA) systematic reviews are conducted to summarize evidence on the accuracy of a diagnostic test including a critical evaluation of the primary studies. Where appropriate, the evidence is meta-analyzed to obtain pooled estimates of effectiveness. In this study, we reviewed and critiqued three DTA guidance documents with…
Descriptors: Diagnostic Tests, Accuracy, Meta Analysis, Graphs
Robert C. Lorenz; Mirjam Jenny; Anja Jacobs; Katja Matthias – Research Synthesis Methods, 2024
Conducting high-quality overviews of reviews (OoR) is time-consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well-established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15-32 min per application. To save time,…
Descriptors: Decision Making, Time Management, Evaluation Methods, Quality Assurance
Qusai Khraisha; Sophie Put; Johanna Kappenberg; Azza Warraitch; Kristin Hadfield – Research Synthesis Methods, 2024
Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained…
Descriptors: Peer Evaluation, Research Reports, Artificial Intelligence, Computer Software
Turner, Simon Lee; Korevaar, Elizabeth; Cumpston, Miranda S.; Kanukula, Raju; Forbes, Andrew B.; McKenzie, Joanne E. – Research Synthesis Methods, 2023
Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide…
Descriptors: Quasiexperimental Design, Graphs, Accuracy, Computation
Shifeng Liu; Florence T. Bourgeois; Claire Narang; Adam G. Dunn – Research Synthesis Methods, 2024
Searching for trials is a key task in systematic reviews and a focus of automation. Previous approaches required knowing examples of relevant trials in advance, and most methods are focused on published trial articles. To complement existing tools, we compared methods for finding relevant trial registrations given a International Prospective…
Descriptors: Artificial Intelligence, Medical Research, Experimental Groups, Control Groups
Jap, Jens; Saldanha, Ian J.; Smith, Bryant T.; Lau, Joseph; Schmid, Christopher H.; Li, Tianjing – Research Synthesis Methods, 2019
Introduction: During systematic reviews, data abstraction is labor- and time-intensive and error-prone. Existing data abstraction systems do not track specific locations and contexts of abstracted information. To address this limitation, we developed a software application, the Data Abstraction Assistant (DAA) and surveyed early users about their…
Descriptors: Computer Software, Literature Reviews, Data Analysis, Usability
Qi, Xinyue; Zhou, Shouhao; Wang, Yucai; Peterson, Christine – Research Synthesis Methods, 2022
Meta-analysis allows researchers to combine evidence from multiple studies, making it a powerful tool for synthesizing information on the safety profiles of new medical interventions. There is a critical need to identify subgroups at high risk of experiencing treatment-related toxicities. However, this remains quite challenging from a statistical…
Descriptors: Bayesian Statistics, Identification, Meta Analysis, Data Analysis
Boon, Michele Hilton; Thomson, Hilary – Research Synthesis Methods, 2021
Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot…
Descriptors: Visualization, Data Analysis, Measurement Techniques, Guidance
Proctor, Tanja; Zimmermann, Samuel; Seide, Svenja; Kieser, Meinhard – Research Synthesis Methods, 2022
During drug development, a biomarker is sometimes identified as separating a patient population into those with more and those with less benefit from evaluated treatments. Consequently, later studies might be targeted, while earlier ones are performed in mixed patient populations. This poses a challenge in evidence synthesis, especially if only…
Descriptors: Comparative Analysis, Meta Analysis, Patients, Medical Research
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