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dc.contributor.authorExternal author(s) only
dc.identifier.citationTaylor KS, Mahtani KR, Aronson JKDealing with categorical risk data when extracting data for meta-analysis. BMJ Evidence-Based Medicine Published Online First: 13 January 2021en
dc.description.abstractA common problem in meta-analysis of observational studies arises when the exposure variable is categorical rather than continuous. These data may be referred to as quantile or quintile data (depending on the number of categories) or dose–response data, and in this article, the term ‘categorical risk data’ will be used. These data may be reported to reflect the increase in cardiovascular risk associated with increasing weight gain, alcohol consumption or frequency of smoking. Further problems arise when studies divide the exposure variable into different numbers of categories, or the same number of categories, but using different thresholds, when data are missing, or when studies include different reference categories. These problems make it difficult to combine data in meta-analysis, but there are methods that can deal with these problems.en
dc.description.sponsorshipSupported by the NIHRen
dc.description.urihttps:// doi: 10.1136/bmjebm-2020-111649en
dc.subjectRisk Assessmenten
dc.titleDealing with categorical risk data when extracting data for meta-analysisen

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