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dc.contributor.authorOrsini, Nicola
dc.contributor.authorLi, Ruifeng
dc.contributor.authorWolk, Alicja
dc.contributor.authorKhudyakov, Polyna
dc.contributor.authorSpiegelman, Donna
dc.date.accessioned2019-09-21T16:11:32Z
dc.date.issued2012
dc.identifier.citationOrsini, Nicola, Ruifeng Li, Alicja Wolk, Polyna Khudyakov, and Donna Spiegelman. 2011. “Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software.” American Journal of Epidemiology 175 (1): 66–73. https://doi.org/10.1093/aje/kwr265.
dc.identifier.issn0002-9262
dc.identifier.issn1476-6256
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41384716*
dc.description.abstractTwo methods for point and interval estimation of relative risk for log-linear exposure-response relations in meta-analyses of published ordinal categorical exposure-response data have been proposed. The authors compared the results of a meta-analysis of published data using each of the 2 methods with the results that would be obtained if the primary data were available and investigated the circumstances under which the approximations required for valid use of each meta-analytic method break down. They then extended the methods to handle nonlinear exposure-response relations. In the present article, methods are illustrated using studies of the relation between alcohol consumption and colorectal and lung cancer risks from the ongoing Pooling Project of Prospective Studies of Diet and Cancer. In these examples, the differences between the results of a meta-analysis of summarized published data and the pooled analysis of the individual original data were small. However, incorrectly assuming no correlation between relative risk estimates for exposure categories from the same study gave biased confidence intervals for the trend and biased P values for the tests for nonlinearity and between-study heterogeneity when there was strong confounding by other model covariates. The authors illustrate the use of 2 publicly available user-friendly programs (Stata and SAS) to implement meta-analysis for dose-response data.
dc.language.isoen_US
dc.publisherOxford University Press
dash.licenseMETA_ONLY
dc.titleMeta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software
dc.typeJournal Article
dc.description.versionVersion of Record
dc.relation.journalAmerican Journal of Epidemiology
dash.depositing.authorSpiegelman, Donna::37eeac21962b33e4e46e7aedde542849::600
dc.date.available2019-09-21T16:11:32Z
dash.workflow.comments1Science Serial ID 7766
dc.identifier.doi10.1093/aje/kwr265
dash.source.volume175;1
dash.source.page66


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