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van Aert, Robbie C. M. – Research Synthesis Methods, 2023
The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta-analysis, but two of the assumptions of the common equal-effect and random-effects meta-analysis model are by definition violated.…
Descriptors: Correlation, Meta Analysis, Sampling, Simulation
Sanghyun Hong; W. Robert Reed – Research Synthesis Methods, 2024
This study builds on the simulation framework of a recent paper by Stanley and Doucouliagos ("Research Synthesis Methods" 2023;14;515--519). S&D use simulations to make the argument that meta-analyses using partial correlation coefficients (PCCs) should employ a "suboptimal" estimator of the PCC standard error when…
Descriptors: Meta Analysis, Correlation, Weighted Scores, Simulation
Pustejovsky, James E.; Rodgers, Melissa A. – Research Synthesis Methods, 2019
Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as the…
Descriptors: Effect Size, Regression (Statistics), Statistical Analysis, Error of Measurement
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
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
Saluja, Ronak; Cheng, Sierra; delos Santos, Keemo Althea; Chan, Kelvin K. W. – Research Synthesis Methods, 2019
Objective: Various statistical methods have been developed to estimate hazard ratios (HRs) from published Kaplan-Meier (KM) curves for the purpose of performing meta-analyses. The objective of this study was to determine the reliability, accuracy, and precision of four commonly used methods by Guyot, Williamson, Parmar, and Hoyle and Henley.…
Descriptors: Meta Analysis, Reliability, Accuracy, Randomized Controlled Trials
Debray, Thomas P. A.; Moons, Karel G. M.; Riley, Richard D. – Research Synthesis Methods, 2018
Small-study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size,…
Descriptors: Meta Analysis, Comparative Analysis, Publications, Bias
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