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Rrita Zejnullahi; Larry V. Hedges – Research Synthesis Methods, 2024
Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods…
Descriptors: Robustness (Statistics), Meta Analysis, Sample Size, Computation
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Jansen, Katrin; Holling, Heinz – Research Synthesis Methods, 2023
In meta-analyses of rare events, it can be challenging to obtain a reliable estimate of the pooled effect, in particular when the meta-analysis is based on a small number of studies. Recent simulation studies have shown that the beta-binomial model is a promising candidate in this situation, but have thus far only investigated its performance in a…
Descriptors: Bayesian Statistics, Meta Analysis, Probability, Simulation
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Bakbergenuly, Ilyas; Hoaglin, David C.; Kulinskaya, Elena – Research Synthesis Methods, 2020
In random-effects meta-analysis the between-study variance ([tau][superscript 2]) has a key role in assessing heterogeneity of study-level estimates and combining them to estimate an overall effect. For odds ratios the most common methods suffer from bias in estimating [tau][superscript 2] and the overall effect and produce confidence intervals…
Descriptors: Meta Analysis, Statistical Bias, Intervals, Sample Size
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Li, Xinru; Dusseldorp, Elise; Meulman, Jacqueline J. – Research Synthesis Methods, 2019
In meta-analytic studies, there are often multiple moderators available (eg, study characteristics). In such cases, traditional meta-analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high-order interactions. To overcome this problem, meta-CART was proposed: an approach that applies…
Descriptors: Correlation, Meta Analysis, Identification, Testing
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Doleman, Brett; Freeman, Suzanne C.; Lund, Jonathan N.; Williams, John P.; Sutton, Alex J. – Research Synthesis Methods, 2020
This study aimed to determine for continuous outcomes dependent on baseline risk, whether funnel plot asymmetry may be due to statistical artefact rather than publication bias and evaluate a novel test to resolve this. Firstly, we conducted assessment for publication bias in nine meta-analyses of postoperative analgesics (344 trials with 25 348…
Descriptors: Outcomes of Treatment, Risk, Publications, Bias
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Papadimitropoulou, Katerina; Stijnen, Theo; Dekkers, Olaf M.; le Cessie, Saskia – Research Synthesis Methods, 2019
The vast majority of meta-analyses uses summary/aggregate data retrieved from published studies in contrast to meta-analysis of individual participant data (IPD). When the outcome is continuous and IPD are available, linear mixed modelling methods can be employed in a one-stage approach. This allows for flexible modelling of within-study…
Descriptors: Meta Analysis, Outcome Measures, Hierarchical Linear Modeling, Sample Size
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Kosch, Robin; Jung, Klaus – Research Synthesis Methods, 2019
Research synthesis, eg, by meta-analysis, is more and more considered in the area of high-dimensional data from molecular research such as gene and protein expression data, especially because most studies and experiments are performed with very small sample sizes. In contrast to most clinical and epidemiological trials, raw data are often…
Descriptors: Genetics, Meta Analysis, Molecular Structure, Scientific Research
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Veroniki, Areti Angeliki; Pavlides, Marios; Patsopoulos, Nikolaos A.; Salanti, Georgia – Research Synthesis Methods, 2013
A problem that is frequently encountered during the systematic review process is when studies that meet the inclusion criteria do not provide the appropriate numerical estimates to include in a meta-analysis. For dichotomous outcomes, a method has been suggested by Di Pietrantonj for reconstructing the 2 × 2 table when the Odds Ratio…
Descriptors: Meta Analysis, Tables (Data), Statistical Analysis, Error of Measurement