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Direct linkHansen, Spencer; Rice, Kenneth – Research Synthesis Methods, 2022
Meta-analysis of proportions is conceptually simple: Faced with a binary outcome in multiple studies, we seek inference on some overall proportion of successes/failures. Under common effect models, exact inference has long been available, but is not when we more realistically allow for heterogeneity of the proportions. Instead a wide range of…
Descriptors: Meta Analysis, Effect Size, Statistical Inference, Intervals
Mathur, Maya B.; VanderWeele, Tyler J. – Research Synthesis Methods, 2021
Meta-regression analyses usually focus on estimating and testing differences in average effect sizes between individual levels of each meta-regression covariate in turn. These metrics are useful but have limitations: they consider each covariate individually, rather than in combination, and they characterize only the mean of a potentially…
Descriptors: Regression (Statistics), Meta Analysis, Effect Size, Computation
Brannick, Michael T.; French, Kimberly A.; Rothstein, Hannah R.; Kiselica, Andrew M.; Apostoloski, Nenad – Research Synthesis Methods, 2021
Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance. In random-effects meta-analysis, tolerance intervals should contain researcher-specified proportions of underlying population effect sizes. Using Monte Carlo simulation, we investigated…
Descriptors: Meta Analysis, Credibility, Intervals, Effect Size
Domínguez Islas, Clara; Rice, Kenneth M. – Research Synthesis Methods, 2022
Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about…
Descriptors: Bayesian Statistics, Evidence, Meta Analysis, Statistical Inference
Weber, Frank; Knapp, Guido; Glass, Änne; Kundt, Günther; Ickstadt, Katja – Research Synthesis Methods, 2021
There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…
Descriptors: Meta Analysis, Computation, Intervals, Statistical Analysis
Held, Leonhard; Matthews, Robert; Ott, Manuela; Pawel, Samuel – Research Synthesis Methods, 2022
It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…
Descriptors: Bayesian Statistics, Statistical Inference, Hypothesis Testing, Credibility
