Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: a Monte Carlo simulation study

Blázquez Rincón, Desirée; Sánchez Meca, Julio; Botella, Juan; Suero, Manuel

Publication:
Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: a Monte Carlo simulation study

dc.contributor.author	Blázquez Rincón, Desirée
dc.contributor.author	Sánchez Meca, Julio
dc.contributor.author	Botella, Juan
dc.contributor.author	Suero, Manuel
dc.contributor.department	Psicología Básica y Metodología
dc.date.accessioned	2025-01-30T09:49:36Z
dc.date.available	2025-01-30T09:49:36Z
dc.date.issued	2023-01-17
dc.description	© 2023, The Author(s). This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published version of a Published Work that appeared in final form in BMC Medical Research Methodology. To access the final edited and published work see https://doi.org/10.1186/s12874-022-01809-0	es
dc.description.abstract	Background Advantages of meta-analysis depend on the assumptions underlying the statistical procedures used being met. One of the main assumptions that is usually taken for granted is the normality underlying the population of true effects in a random-effects model, even though the available evidence suggests that this assumption is often not met. This paper examines how 21 frequentist and 24 Bayesian methods, including several novel procedures, for computing a point estimate of the heterogeneity parameter ( ) perform when the distribution of random effects departs from normality compared to normal scenarios in meta-analysis of standardized mean differences. Methods A Monte Carlo simulation was carried out using the R software, generating data for meta-analyses using the standardized mean difference. The simulation factors were the number and average sample size of primary studies, the amount of heterogeneity, as well as the shape of the random-effects distribution. The point estimators were compared in terms of absolute bias and variance, although results regarding mean squared error were also discussed. Results Although not all the estimators were affected to the same extent, there was a general tendency to obtain lower and more variable estimates as the random-effects distribution departed from normality. However, the estimators ranking in terms of their absolute bias and variance did not change: Those estimators that obtained lower bias also showed greater variance. Finally, a large number and sample size of primary studies acted as a bias-protective factor against a lack of normality for several procedures, whereas only a high number of studies was a variance-protective factor for most of the estimators analyzed. Conclusions Although the estimation and inference of the combined effect have proven to be sufficiently robust, our work highlights the role that the deviation from normality may be playing in the meta-analytic conclusions from the simulation results and the numerical examples included in this work. With the aim to exercise caution in the interpretation of the results obtained from random-effects models, the tau2() R function is made available for obtaining the range of values computed from the 45 estimators analyzed in this work, as well as to assess how the pooled effect, its confidence and prediction intervals vary according to the estimator chosen.	es
dc.format	application/pdf	es
dc.format.extent	22	es
dc.identifier.citation	BMC Medical Research Methodology, 2023, Vol. 23 : 19
dc.identifier.doi	https://doi.org/10.1186/s12874-022-01809-0
dc.identifier.issn	Electronic: 1471-2288
dc.identifier.uri	http://hdl.handle.net/10201/149743
dc.language	eng	es
dc.publisher	BioMed Central	es
dc.relation	Grant PID2019-104080GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.	es
dc.relation.publisherversion	https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-022-01809-0	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Heterogeneity	es
dc.subject	Non normality	es
dc.subject	Random effects	es
dc.subject	Meta analysis	es
dc.subject	Between study variance	es
dc.subject	Simulation study	es
dc.title	Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: a Monte Carlo simulation study	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es

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