Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation
| dc.contributor.author | Patsopoulos, N. A. | en |
| dc.contributor.author | Evangelou, E. | en |
| dc.contributor.author | Ioannidis, J. P. | en |
| dc.date.accessioned | 2015-11-24T18:35:37Z | |
| dc.date.available | 2015-11-24T18:35:37Z | |
| dc.identifier.issn | 0300-5771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/17117 | |
| dc.rights | Default Licence | - |
| dc.subject | heterogeneity | en |
| dc.subject | sensitivity analysis | en |
| dc.subject | sequential algorithm | en |
| dc.subject | meta-analysis | en |
| dc.subject | individual patient data | en |
| dc.subject | meta-regression | en |
| dc.subject | exploring heterogeneity | en |
| dc.subject | genetic association | en |
| dc.subject | systematic reviews | en |
| dc.subject | clinical-trials | en |
| dc.subject | level | en |
| dc.title | Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation | en |
| heal.abstract | Background Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. Methods We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >= 4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I(2) thresholds were used (50% and 25%). Results Both algorithms have succeeded in achieving the pre-specified final I(2) thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I(2) > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I(2) < 50. Similarly, among meta-analyses with initial I(2) 25, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even < 25%. The number of excluded studies correlated modestly with initial estimated I(2) (correlation coefficients 0.52-0.68 depending on algorithm used). Conclusions The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the heterogeneity. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | Doi 10.1093/Ije/Dyn065 | - |
| heal.identifier.secondary | <Go to ISI>://000259771500031 | - |
| heal.identifier.secondary | http://ije.oxfordjournals.org/content/37/5/1148.full.pdf | - |
| heal.journalName | Int J Epidemiol | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2008 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών και Τεχνολογιών. Τμήμα Βιολογικών Εφαρμογών και Τεχνολογιών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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