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dc.contributor.authorFrank, Anna-Simone
dc.contributor.authorMatteson, David S.
dc.contributor.authorSolvang, Hiroko Kato
dc.contributor.authorLupattelli, Angela
dc.contributor.authorNordeng, Hedvig Marie Egeland
dc.date.accessioned2021-09-10T08:50:45Z
dc.date.available2021-09-10T08:50:45Z
dc.date.created2020-06-07T14:58:57Z
dc.date.issued2020
dc.identifier.citationEpidemiologic Methods. 2020, 9 (1), .
dc.identifier.issn2194-9263
dc.identifier.urihttps://hdl.handle.net/11250/2775155
dc.description.abstractThis manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.
dc.language.isoeng
dc.titleExtending balance assessment for the generalized propensity score under multiple imputation
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionacceptedVersion
dc.description.versionacceptedVersion
dc.source.pagenumber17
dc.source.volume9
dc.source.journalEpidemiologic Methods
dc.source.issue1
dc.identifier.doi10.1515/em-2019-0003
dc.identifier.cristin1814222
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextpostprint
cristin.qualitycode1


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