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Cairns, Maxwell; Prendergast, Luke A. – Research Synthesis Methods, 2022
As a measure of heterogeneity in meta-analysis, the coefficient of variation (CV) has been recently considered, providing researchers with a complement to the very popular I[superscript 2] measure. While I[superscript 2] measures the proportion of total variance that is due to variance of the random effects, the CV is the ratio of the standard…
Descriptors: Meta Analysis, Statistical Analysis, Intervals, Computation
Jiang, Ziren; Cao, Wenhao; Chu, Haitao; Bazerbachi, Fateh; Siegel, Lianne – Research Synthesis Methods, 2023
A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference…
Descriptors: Intervals, Computation, Meta Analysis, Measurement
Kulinskaya, Elena; Mah, Eung Yaw – Research Synthesis Methods, 2022
To present time-varying evidence, cumulative meta-analysis (CMA) updates results of previous meta-analyses to incorporate new study results. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Statistical Bias
Xu, Chang; Furuya-Kanamori, Luis; Lin, Lifeng – Research Synthesis Methods, 2022
In evidence synthesis, dealing with zero-events studies is an important and complicated task that has generated broad discussion. Numerous methods provide valid solutions to synthesizing data from studies with zero-events, either based on a frequentist or a Bayesian framework. Among frequentist frameworks, the one-stage methods have their unique…
Descriptors: Evidence, Synthesis, Statistical Analysis, Meta Analysis
Henmi, Masayuki; Hattori, Satoshi; Friede, Tim – Research Synthesis Methods, 2021
In meta-analyses including only few studies, the estimation of the between-study heterogeneity is challenging. Furthermore, the assessment of publication bias is difficult as standard methods such as visual inspection or formal hypothesis tests in funnel plots do not provide adequate guidance. Previously, Henmi and Copas (Statistics in Medicine…
Descriptors: Statistical Analysis, Computation, Bias, Publications
Rücker, Gerta; Schwarzer, Guido – Research Synthesis Methods, 2021
In the era of the "reproducibility crisis" and the "P-value controversy" new ways of presentation and interpretation of the results of a meta-analysis are desirable. One suggestion that has been made for single studies almost six decades ago and taken up now and then is the P-value function. For a given outcome, this function…
Descriptors: Meta Analysis, Statistical Analysis, Computation, Replication (Evaluation)
Cao, Wenhao; Siegel, Lianne; Zhou, Jincheng; Zhu, Motao; Tong, Tiejun; Chen, Yong; Chu, Haitao – Research Synthesis Methods, 2021
A reference interval provides a basis for physicians to determine whether a measurement is typical of a healthy individual. It can be interpreted as a prediction interval for a new individual from the overall population. However, a reference interval based on a single study may not be representative of the broader population. Meta-analysis can…
Descriptors: Meta Analysis, Statistical Analysis, Intervals, Computation
Riley, Richard D.; Collins, Gary S.; Hattle, Miriam; Whittle, Rebecca; Ensor, Joie – Research Synthesis Methods, 2023
Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here,…
Descriptors: Computation, Meta Analysis, Participant Characteristics, Data
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
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
Huang, Ao; Komukai, Sho; Friede, Tim; Hattori, Satoshi – Research Synthesis Methods, 2021
Prospective registration of study protocols in clinical trial registries is a useful way to minimize the risk of publication bias in meta-analysis, and several clinical trial registries are available nowadays. However, they are mainly used as a tool for searching studies and information submitted to the registries has not been utilized as…
Descriptors: Publications, Bias, Meta Analysis, Selection
Bom, Pedro R. D.; Rachinger, Heiko – Research Synthesis Methods, 2020
Meta-studies are often conducted on empirical findings obtained from overlapping samples. Sample overlap is common in research fields that strongly rely on aggregated observational data (eg, economics and finance), where the same set of data may be used in several studies. More generally, sample overlap tends to occur whenever multiple estimates…
Descriptors: Meta Analysis, Sampling, Research Problems, Computation
Poom, Leo; af Wåhlberg, Anders – Research Synthesis Methods, 2022
In meta-analysis, effect sizes often need to be converted into a common metric. For this purpose conversion formulas have been constructed; some are exact, others are approximations whose accuracy has not yet been systematically tested. We performed Monte Carlo simulations where samples with pre-specified population correlations between the…
Descriptors: Meta Analysis, Effect Size, Mathematical Formulas, Monte Carlo Methods
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
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
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