Propensity score matching after multiple imputation when a confounder has missing data
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | SEGALAS, Corentin | |
| dc.contributor.author | LEYRAT, Clémence | |
| dc.contributor.author | CARPENTER, James | |
| dc.contributor.author | WILLIAMSON, Elizabeth | |
| dc.date.accessioned | 2024-09-24T07:38:33Z | |
| dc.date.available | 2024-09-24T07:38:33Z | |
| dc.date.issued | 2023-01-25 | |
| dc.identifier.issn | 0277-6715 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/201760 | |
| dc.description.abstractEn | One of the main challenges when using observational data for causal inference is the presence of confounding. A classic approach to account for confounding is the use of propensity score techniques that provide consistent estimators of the causal treatment effect under four common identifiability assumptions for causal effects, including that of no unmeasured confounding. Propensity score matching is a very popular approach which, in its simplest form, involves matching each treated patient to an untreated patient with a similar estimated propensity score, that is, probability of receiving the treatment. The treatment effect can then be estimated by comparing treated and untreated patients within the matched dataset. When missing data arises, a popular approach is to apply multiple imputation to handle the missingness. The combination of propensity score matching and multiple imputation is increasingly applied in practice.However, in this article we demonstrate that combining multiple imputation and propensity score matching can lead to over-coverage of the confidence interval for the treatment effect estimate. We explore the cause of this over-coverage and we evaluate, in this context, the performance of a correction to Rubin's rules for multiple imputation proposed by finding that this correction removes the over-coverage. | |
| dc.language.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject.en | confounding missing data multiple imputation propensity score matching | |
| dc.subject.en | confounding | |
| dc.subject.en | missing data | |
| dc.subject.en | multiple imputation | |
| dc.subject.en | propensity score matching | |
| dc.title.en | Propensity score matching after multiple imputation when a confounder has missing data | |
| dc.title.alternative | Stat Med | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1002/sim.9658 | en_US |
| dc.subject.hal | Statistiques [stat]/Méthodologie [stat.ME] | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 36695043 | en_US |
| bordeaux.journal | Statistics in Medicine | en_US |
| bordeaux.page | 1082 - 1095 | en_US |
| bordeaux.volume | 42 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 7 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.institution | INRIA | en_US |
| bordeaux.team | SISTM_BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-04693080 | |
| hal.version | 1 | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Statistics%20in%20Medicine&rft.date=2023-01-25&rft.volume=42&rft.issue=7&rft.spage=1082%20-%201095&rft.epage=1082%20-%201095&rft.eissn=0277-6715&rft.issn=0277-6715&rft.au=SEGALAS,%20Corentin&LEYRAT,%20Cl%C3%A9mence&CARPENTER,%20James&WILLIAMSON,%20Elizabeth&rft.genre=article |
