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Campbell, Harlan; de Jong, Valentijn M. T.; Maxwell, Lauren; Jaenisch, Thomas; Debray, Thomas P. A.; Gustafson, Paul – Research Synthesis Methods, 2021
Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or…
Descriptors: Error of Measurement, Meta Analysis, Bayesian Statistics, Statistical Analysis
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Van Lissa, Caspar J.; van Erp, Sara; Clapper, Eli-Boaz – Research Synthesis Methods, 2023
When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…
Descriptors: Bayesian Statistics, Regression (Statistics), Maximum Likelihood Statistics, Meta Analysis
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Huang, Hening – Research Synthesis Methods, 2023
Many statistical methods (estimators) are available for estimating the consensus value (or average effect) and heterogeneity variance in interlaboratory studies or meta-analyses. These estimators are all valid because they are developed from or supported by certain statistical principles. However, no estimator can be perfect and must have error or…
Descriptors: Statistical Analysis, Computation, Measurement Techniques, Meta Analysis
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van Zundert, Camiel H. J.; Miocevic, Milica – Research Synthesis Methods, 2020
Synthesizing findings about the indirect (mediated) effect plays an important role in determining the mechanism through which variables affect one another. This simulation study compared six methods for synthesizing indirect effects: correlation-based MASEM, parameter-based MASEM, marginal likelihood synthesis, an adjustment to marginal likelihood…
Descriptors: Correlation, Comparative Analysis, Meta Analysis, Bayesian Statistics
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Seide, Svenja E.; Jensen, Katrin; Kieser, Meinhard – Research Synthesis Methods, 2020
The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between-trial heterogeneity and including multi-arm…
Descriptors: Bayesian Statistics, Meta Analysis, Data Analysis, Networks
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Hong, Hwanhee; Chu, Haitao; Zhang, Jing; Carlin, Bradley P. – Research Synthesis Methods, 2016
Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular because of their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (although richer than standard meta-analysis, comparing only two…
Descriptors: Bayesian Statistics, Meta Analysis, Outcomes of Treatment, Comparative Analysis