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Siegel, Lianne; Chu, Haitao – Research Synthesis Methods, 2023
Reference intervals, or reference ranges, aid medical decision-making by containing a pre-specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta-analysis based on a random effects model: a frequentist approach, a Bayesian…
Descriptors: Bayesian Statistics, Meta Analysis, Intervals, Decision Making
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Godolphin, Peter J.; White, Ian R.; Tierney, Jayne F.; Fisher, David J. – Research Synthesis Methods, 2023
Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute…
Descriptors: Meta Analysis, Outcomes of Treatment, Medical Research, Comparative Analysis
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Efthimiou, Orestis; White, Ian R. – Research Synthesis Methods, 2020
Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we…
Descriptors: Models, Meta Analysis, Network Analysis, Simulation
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Seide, Svenja E.; Jensen, Katrin; Kieser, Meinhard – Research Synthesis Methods, 2021
Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The…
Descriptors: Graphs, Visualization, Simulation, Meta Analysis
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Noma, Hisashi; Gosho, Masahiko; Ishii, Ryota; Oba, Koji; Furukawa, Toshi A. – Research Synthesis Methods, 2020
Network meta-analysis has been gaining prominence as an evidence synthesis method that enables the comprehensive synthesis and simultaneous comparison of multiple treatments. In many network meta-analyses, some of the constituent studies may have markedly different characteristics from the others, and may be influential enough to change the…
Descriptors: Networks, Meta Analysis, Evidence, Comparative Analysis
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Suurmond, Robert; van Rhee, Henk; Hak, Tony – Research Synthesis Methods, 2017
We present a new tool for meta-analysis, "Meta-Essentials," which is free of charge and easy to use. In this paper, we introduce the tool and compare its features to other tools for meta-analysis. We also provide detailed information on the validation of the tool. Although free of charge and simple, "Meta-Essentials"…
Descriptors: Meta Analysis, Research Tools, Comparative Analysis, Validity
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Jacobs, Perke; Viechtbauer, Wolfgang – Research Synthesis Methods, 2017
Meta-analyses are often used to synthesize the findings of studies examining the correlational relationship between two continuous variables. When only dichotomous measurements are available for one of the two variables, the biserial correlation coefficient can be used to estimate the product-moment correlation between the two underlying…
Descriptors: Sampling, Correlation, Meta Analysis, Measurement
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Stevens, John W.; Fletcher, Christine; Downey, Gerald; Sutton, Anthea – Research Synthesis Methods, 2018
A network meta-analysis allows a simultaneous comparison between treatments evaluated in randomised controlled trials that share at least one treatment with at least one other study. Estimates of treatment effects may be required for treatments across disconnected networks of evidence, which requires a different statistical approach and modelling…
Descriptors: Meta Analysis, Network Analysis, Comparative Analysis, Outcomes of Treatment
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Schmitz, Tom; Bukowski, Mark; Koschmieder, Steffen; Schmitz-Rode, Thomas; Farkas, Robert – Research Synthesis Methods, 2019
Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the…
Descriptors: Innovation, Biomedicine, Medical Research, Meta Analysis
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Holmes, Eileen M.; Leahy, Joy; Walsh, Cathal D.; White, Arthur; Donnan, Peter T.; Lamrock, Felicity – Research Synthesis Methods, 2019
Indirect treatment comparisons are useful to estimate relative treatment effects when head-to-head studies are not conducted. Statisticians at the National Centre for Pharmacoeconomics Ireland (NCPE) and Scottish Medicines Consortium (SMC) assess the clinical and cost-effectiveness of new medicines as part of multidisciplinary teams. We describe…
Descriptors: Decision Making, Drug Therapy, Comparative Analysis, Pharmacology
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van Valkenhoef, Gert; Dias, Sofia; Ades, A. E.; Welton, Nicky J. – Research Synthesis Methods, 2016
Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach,…
Descriptors: Networks, Meta Analysis, Automation, Models
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Melendez-Torres, G. J.; Sutcliffe, Katy; Burchett, Helen E. D.; Rees, Rebecca; Thomas, James – Research Synthesis Methods, 2019
Qualitative comparative analysis (QCA) was originally developed as a tool for cross-national comparisons in macrosociology, but its use in evaluation and evidence synthesis of complex interventions is rapidly developing. QCA is theory-driven and relies on Boolean logic to identify pathways to an outcome (e.g., is the intervention effective or…
Descriptors: Comparative Analysis, Intervention, Evidence, Logical Thinking