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dc.contributor.authorBlake, Helen Aen
dc.contributor.authorLeyrat, Clémenceen
dc.contributor.authorMansfield, Kathryn Een
dc.contributor.authorSeaman, Shaunen
dc.contributor.authorTomlinson, Laurie Aen
dc.contributor.authorCarpenter, Jamesen
dc.contributor.authorWilliamson, Elizabeth Jen
dc.date.accessioned2020-02-11T00:30:19Z
dc.date.available2020-02-11T00:30:19Z
dc.date.issued2020-05en
dc.identifier.issn0277-6715
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301919
dc.description.abstractElectronic health records are a valuable data source for investigating health related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study – using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury – two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this paper,we explore the MPA’s underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated.We present a framework providing practical guidance for assessing whether the MPA’s assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate.We apply our framework to our motivating study, showing that the MPA’s underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.
dc.description.sponsorshipEconomic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.publisherWiley-Blackwell
dc.rightsAll rights reserved
dc.rights.uri
dc.titlePropensity scores using missingness pattern information: a practical guide.en
dc.typeArticle
prism.endingPage1657
prism.issueIdentifier11en
prism.publicationDate2020en
prism.publicationNameStatistics in medicineen
prism.startingPage1641
prism.volume39en
dc.identifier.doi10.17863/CAM.48996
dcterms.dateAccepted2020-01-20en
rioxxterms.versionofrecord10.1002/sim.8503en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-05en
dc.contributor.orcidBlake, Helen A [0000-0001-7856-5931]
dc.contributor.orcidLeyrat, Clémence [0000-0002-4097-4577]
dc.contributor.orcidSeaman, Shaun [0000-0003-3726-5937]
dc.contributor.orcidTomlinson, Laurie A [0000-0001-8848-9493]
dc.contributor.orcidWilliamson, Elizabeth J [0000-0001-6905-876X]
dc.identifier.eissn1097-0258
rioxxterms.typeJournal Article/Reviewen
cam.orpheus.successTue Mar 31 10:36:45 BST 2020 - Embargo updated*
rioxxterms.freetoread.startdate2021-02-27


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