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Frömke, Cornelia; Kirstein, Mathia; Zapf, Antonia – Research Synthesis Methods, 2022
The accuracy of a diagnostic test is often expressed using a pair of measures: sensitivity (proportion of test positives among all individuals with target condition) and specificity (proportion of test negatives among all individuals without target condition). If the outcome of a diagnostic test is binary, results from different studies can easily…
Descriptors: Accuracy, Diagnostic Tests, Meta Analysis, Statistical Analysis
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Kulinskaya, Elena; Hoaglin, David C. – Research Synthesis Methods, 2023
For estimation of heterogeneity variance T[superscript 2] in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized Q statistic, Q[subscript F], in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators…
Descriptors: Q Methodology, Statistical Analysis, Meta Analysis, Intervals
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Bramley, Paul; López-López, José A.; Higgins, Julian P. T. – Research Synthesis Methods, 2021
Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions…
Descriptors: Meta Analysis, Bias, Research Problems, Computation
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van Aert, Robbie C. M.; van Assen, Marcel A. L. M.; Viechtbauer, Wolfgang – Research Synthesis Methods, 2019
The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between-study variance…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Effect Size
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Günhan, Burak Kürsad; Friede, Tim; Held, Leonhard – Research Synthesis Methods, 2018
Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network…
Descriptors: Meta Analysis, Regression (Statistics), Statistical Inference, Probability
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Mathes, Tim; Kuss, Oliver – Research Synthesis Methods, 2018
Meta-analyses often include only a small number of studies ([less than or equal to]5). Estimating between-study heterogeneity is difficult in this situation. An inaccurate estimation of heterogeneity can result in biased effect estimates and too narrow confidence intervals. The beta-binominal model has shown good statistical properties for…
Descriptors: Comparative Analysis, Meta Analysis, Probability, Statistical Analysis