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dc.contributor.authorYiu, Sean
dc.contributor.authorSu, Li
dc.date.accessioned2022-03-31T20:00:11Z
dc.date.available2022-03-31T20:00:11Z
dc.date.issued2022-03
dc.date.submitted2019-04-22
dc.identifier.issn0006-341X
dc.identifier.otherbiom13411
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335628
dc.description.abstractMarginal structural models (MSMs) with inverse probability weighted estimators (IPWEs) are widely used to estimate causal effects of treatment sequences on longitudinal outcomes in the presence of time-varying confounding and dependent censoring. However, IPWEs for MSMs can be inefficient and unstable if weights are estimated by maximum likelihood. To improve the performance of IPWEs, covariate balancing weight (CBW) methods have been proposed and recently extended to MSMs. However, existing CBW methods for MSMs are inflexible for practical use because they often do not handle dependent censoring, nonbinary treatments, and longitudinal outcomes (instead of eventual outcomes at a study end). In this paper, we propose a joint calibration approach to CBW estimation for MSMs that can accommodate (1) both time-varying confounding and dependent censoring, (2) binary and nonbinary treatments, (3) eventual outcomes and longitudinal outcomes. We develop novel calibration restrictions by jointly eliminating covariate associations with both treatment assignment and censoring processes after weighting the observed data sample (i.e., to optimize covariate balance in finite samples). Two different methods are proposed to implement the calibration. Simulations show that IPWEs with calibrated weights perform better than IPWEs with weights from maximum likelihood and the "Covariate Balancing Propensity Score" method. We apply our method to a natural history study of HIV for estimating the effects of highly active antiretroviral therapy on CD4 cell counts over time.
dc.languageen
dc.publisherWiley
dc.subjectBIOMETRIC METHODOLOGY
dc.subjectcalibration
dc.subjectcausal inference
dc.subjectcovariate balancing
dc.subjectdropout
dc.subjectlongitudinal data
dc.subjectpropensity score
dc.titleJoint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models.
dc.typeArticle
dc.date.updated2022-03-31T20:00:10Z
prism.endingPage127
prism.issueIdentifier1
prism.publicationNameBiometrics
prism.startingPage115
prism.volume78
dc.identifier.doi10.17863/CAM.83059
dcterms.dateAccepted2020-10-29
rioxxterms.versionofrecord10.1111/biom.13411
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidYiu, Sean [0000-0002-8345-3632]
dc.contributor.orcidSu, Li [0000-0003-0919-3462]
dc.identifier.eissn1541-0420
pubs.funder-project-idMRC (unknown)
pubs.funder-project-idMRC (unknown)
cam.issuedOnline2020-12-11


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