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Zero- and Few-Shot Prompting of Generative Large Language Models Provides Weak Assessment of Risk of Bias in Clinical Trials
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Simon Šuster; Timothy Baldwin; Karin Verspoor – Research Synthesis Methods, 2024
Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of available training data. In this study, we investigate the effectiveness of generative large language…
Descriptors: Medical Research, Safety, Experimental Groups, Control Groups
Impact of Searching Clinical Trials Registers in Systematic Reviews of Pharmaceutical and Non-Pharmaceutical Interventions: Reanalysis of Meta-Analyses
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Alqaidoom, Zainab; Nguyen, Phi-Yen; Awadh, Maryam; Page, Matthew J. – Research Synthesis Methods, 2023
Systematic reviewers are advised to search trials registers to minimise risk of reporting biases. However, there has been little research on the impact of searching trials registers on the results of meta-analyses. We aimed to evaluate the impact of searching clinical trials registers for systematic reviews of pharmaceutical or non-pharmaceutical…
Descriptors: Experimental Groups, Medical Research, Drug Therapy, Pharmacology
A Comparison of Machine Learning Methods to Find Clinical Trials for Inclusion in New Systematic Reviews from Their PROSPERO Registrations Prior to Searching and Screening
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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
Uncertain about Uncertainty in Matching-Adjusted Indirect Comparisons? A Simulation Study to Compare Methods for Variance Estimation
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Conor O. Chandler; Irina Proskorovsky – Research Synthesis Methods, 2024
In health technology assessment, matching-adjusted indirect comparison (MAIC) is the most common method for pairwise comparisons that control for imbalances in baseline characteristics across trials. One of the primary challenges in MAIC is the need to properly account for the additional uncertainty introduced by the matching process. Limited…
Descriptors: Predictor Variables, Influence of Technology, Evaluation Methods, Methods Research
Opportunities and Challenges in Using Studies without a Control Group in Comparative Effectiveness Reviews
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Paulus, Jessica K.; Dahabreh, Issa J.; Balk, Ethan M.; Avendano, Esther E.; Lau, Joseph; Ip, Stanley – Research Synthesis Methods, 2014
When examining the evidence on therapeutic interventions to answer a comparative effectiveness research question, one should consider all studies that are informative on the interventions' causal effects. "Single group studies" evaluate outcomes longitudinally in cohorts of subjects who are managed with a single treatment strategy.…
Descriptors: Control Groups, Comparative Analysis, Intervention, Experimental Groups
The Impact of Multiple Endpoint Dependency on "Q" and "I"[superscript 2] in Meta-Analysis
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Thompson, Christopher Glen; Becker, Betsy Jane – Research Synthesis Methods, 2014
A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures "Q" and…
Descriptors: Meta Analysis, Effect Size, Correlation, Experimental Groups