Significance of Random-effects model
The random-effects model is a statistical approach utilized in meta-analysis that acknowledges the variability in effect sizes across different studies. It assumes that true effect sizes may differ among research, allowing for enhanced generalization and more accurate aggregation of results. This model is particularly applied when heterogeneity among study outcomes is present, accommodating differences in populations or interventions. By factoring in this variability, the random-effects model contributes to a more nuanced understanding of overall effect sizes in meta-analyses.
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The concept of Random-effects model in scientific sources
The Random-effects model is a statistical method in meta-analysis that accommodates variability in effect sizes across studies, acknowledging that different studies may exhibit different treatment effects due to inherent heterogeneity.
From: World Journal of Pharmaceutical Research
(1) A statistical approach that accounts for variability between studies in a meta-analysis, allowing for different effect sizes across studies.[1] (2) A statistical method used in meta-analysis to account for variability between studies, applied when heterogeneity is observed in outcomes.[2] (3) A statistical model used in meta-analysis that assumes that the underlying effect sizes can vary between studies.[3] (4) A statistical model used in meta-analysis that accounts for variability between studies, suggesting that results may vary due to inherent differences among studies.[4] (5) A statistical method used in meta-analysis that accounts for variations between studies, allowing for better aggregation of results from diverse research.[5]
From: The Malaysian Journal of Medical Sciences
(1) The random-effects model is a statistical method used in meta-analysis to calculate the overall effect size while accounting for variability between different studies.[6]
From: Journal of Ayurveda and Integrative Medicine
(1) The statistical model used for the meta-analysis to account for variability among the studies included.[7]