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Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSILENOU, Bernard C
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAVALOS FERNANDEZ, Marta
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
dc.contributor.authorBERR, Claudine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPARIENTE, Antoine
IDREF: 13395711X
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJACQMIN-GADDA, Helene
dc.date.accessioned2020-07-13T13:10:46Z
dc.date.available2020-07-13T13:10:46Z
dc.date.issued2019-01-31
dc.identifier.issn1932-6203 (Electronic) 1932-6203 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/10441
dc.description.abstractEnBACKGROUND: Studies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort. METHODS: We conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples. RESULTS: When the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders. CONCLUSION: When choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/
dc.subject.enBiostatistics
dc.subject.enLEHA
dc.subject.enSISTM
dc.subject.enPharmacoEpi-Drugs
dc.subject.enFR
dc.title.enHealth administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data
dc.title.alternativePLoS Oneen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1371/journal.pone.0211118en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed30703112en_US
bordeaux.journalPLoS ONEen_US
bordeaux.pagee0211118en_US
bordeaux.volume14en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamLEHA_BPH
bordeaux.teamSISTM_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
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