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Let x be the mean and let s be standard deviation of de data distribution. Issues related to these diagnostic procedures in meta‐analysis are also discussed. Univariate Outliers Given a data set of n observations of a variable x.
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Three examples are used to illustrate the usefulness of these procedures in various research settings.
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The present paper extends standard diagnostic procedures developed for linear regression analyses to the meta‐analytic fixed‐ and random/mixed‐effects models. It has been around for more than 15 years and has been used in hundreds of analyses and publications. The name MIX comes from Meta-analysis In eXcel and 2.0 identifies a major upgrade of the source code a few years back.
Comprehensive meta analysis outliers how to#
While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta‐analysis, limited studies have addressed how to obtain such diagnostic measures in the context of a meta‐analysis. MIX 2.0 is a sophisticated statistical add-in for performing meta-analysis in Excel. The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta‐analysis. Outliers, also known as anomalies, deviants, peculiarities, abnormalities, exceptions, discordant, aberrations, contaminants, and novelties in different domains, are unusual cases that do not fit the general patterns within the data and are significantly different from the rest of the data that are known as inliers or normal instances (Aggarwal, 2016, Foorthuis, 2020). Outlier and influence diagnostics for meta‐analysis Outlier and influence diagnostics for meta‐analysis Metaanalysis was performed using Comprehensive MetaAnalysis Software version 3 (2013, Biostat, Englewood, NJ) calculating the standardized mean difference (SMD) and the 95 confidence interval (95 CI) of the NLR and LCR values in patients with COVID19 with or without severe disease.