Letter to the Editor on detecting and dealing with heterogeneity in meta-analyses by Cordero and Dans
| dc.contributor.author | Hoaglin, David C. | |
| dc.date | 2022-08-11T08:10:37.000 | |
| dc.date.accessioned | 2022-08-23T17:14:26Z | |
| dc.date.available | 2022-08-23T17:14:26Z | |
| dc.date.issued | 2021-06-10 | |
| dc.date.submitted | 2021-10-21 | |
| dc.identifier.citation | <p>Hoaglin DC. Letter to the Editor on detecting and dealing with heterogeneity in meta-analyses by Cordero and Dans. J Clin Epidemiol. 2021 Jun 10:S0895-4356(21)00181-5. doi: 10.1016/j.jclinepi.2021.06.003. Epub ahead of print. PMID: 34118366. <a href="https://doi.org/10.1016/j.jclinepi.2021.06.003">Link to article on publisher's site</a></p> | |
| dc.identifier.issn | 0895-4356 (Linking) | |
| dc.identifier.doi | 10.1016/j.jclinepi.2021.06.003 | |
| dc.identifier.pmid | 34118366 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14038/46954 | |
| dc.description.abstract | Cordero and Dans give valuable advice on various aspects of detecting and dealing with heterogeneity in meta-analyses. For assessing statistical heterogeneity, they start with a forest plot of the study-level effect estimates and complement it with 2 related numerical measures, Q and I2. Other recommended approaches in the literature include the between-study standard deviation and a prediction interval for the effect in a new study. All these approaches have shortcomings, which investigation of heterogeneity should take into account. Surprisingly, the limitations of Q and I2, the 2 most popular, are not yet widely understood. | |
| dc.language.iso | en_US | |
| dc.relation | <p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34118366&dopt=Abstract">Link to Article in PubMed</a></p> | |
| dc.relation.url | https://doi.org/10.1016/j.jclinepi.2021.06.003 | |
| dc.subject | Heterogeneity | |
| dc.subject | Q statistic | |
| dc.subject | I2 | |
| dc.subject | Biostatistics | |
| dc.subject | Clinical Epidemiology | |
| dc.subject | Epidemiology | |
| dc.subject | Health Services Research | |
| dc.title | Letter to the Editor on detecting and dealing with heterogeneity in meta-analyses by Cordero and Dans | |
| dc.type | Letter to the Editor | |
| dc.source.journaltitle | Journal of clinical epidemiology | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/qhs_pp/1433 | |
| dc.identifier.contextkey | 25551198 | |
| html.description.abstract | <p>Cordero and Dans give valuable advice on various aspects of detecting and dealing with heterogeneity in meta-analyses. For assessing statistical heterogeneity, they start with a forest plot of the study-level effect estimates and complement it with 2 related numerical measures, <em>Q</em> and <em>I</em><sup>2</sup>. Other recommended approaches in the literature include the between-study standard deviation and a prediction interval for the effect in a new study. All these approaches have shortcomings, which investigation of heterogeneity should take into account. Surprisingly, the limitations of <em>Q</em> and <em>I</em><sup>2</sup>, the 2 most popular, are not yet widely understood.</p> | |
| dc.identifier.submissionpath | qhs_pp/1433 | |
| dc.contributor.department | Division of Biostatistics and Health Services Research, Department of Population and Quantitative Health Sciences |
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