2008
DOI: 10.1186/1471-2288-8-79
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Undue reliance on I 2 in assessing heterogeneity may mislead

Abstract: Background: The heterogeneity statistic I 2 , interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included.

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Cited by 890 publications
(662 citation statements)
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“…21 We assessed the heterogeneity of study results by the use of t 2 metric. 19,22 Possible sources of heterogeneity were explored through a subgroup and meta-regression technique. The effect of small studies in publication bias was explored by the Egger' s test.…”
Section: Discussionmentioning
confidence: 99%
“…21 We assessed the heterogeneity of study results by the use of t 2 metric. 19,22 Possible sources of heterogeneity were explored through a subgroup and meta-regression technique. The effect of small studies in publication bias was explored by the Egger' s test.…”
Section: Discussionmentioning
confidence: 99%
“…17 We ran a sensitivity analysis on our primary outcome using a Bayesian random-eff ects model with Monte Carlo Markov chain simulations of variability. 18 We calculated the τ² statistic to assess the proportion of overall variation attributable to betweenstudy heterogeneity, because this measure is less sensitive to the number of studies, 19 and explored potential sources of heterogeneity with univariate sensitivity analyses with χ² to estabish the potential eff ect of the following covariates: DST confi rmatory testing (≤50% or >50% of the cohort), treatment duration (≤ 18 months or >18 months), use of injectable drugs (≤80% or >80% of the cohort), age (≤ 5 years or >5 years), and HIV status (≤30% or >30% of the cohort). All p values are two-sided, and a p value of less than 0·05 was regarded as signifi cant.…”
Section: Discussionmentioning
confidence: 99%
“…We measured statistical heterogeneity using the between-study variance t 2 (8,9) and performed subgroup and sensitivity analysis to account for potential sources of heterogeneity. We used t 2 statistic to describe the underlying between-study variability, given that it does not systematically increase with the number or size of studies included in the analysis, whereas I 2 may increase artificially with accumulating evidence (9). The effect of carrier state on ensuing infections was reported as risk ratio (RR) estimate and random effects confidence intervals (95% CI).…”
Section: Methodsmentioning
confidence: 99%