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An Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing Values

dc.contributor.authorPeng, Yahongen_US
dc.contributor.authorLittle, Roderick J. A.en_US
dc.contributor.authorRaghunathan, Trivellore E.en_US
dc.date.accessioned2010-04-01T15:06:13Z
dc.date.available2010-04-01T15:06:13Z
dc.date.issued2004-09en_US
dc.identifier.citationPeng, Yahong; Little, Roderick J. A.; Raghunathan, Trivellore E. (2004). "An Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing Values." Biometrics 60(3): 598-607. <http://hdl.handle.net/2027.42/65568>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65568
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15339281&dopt=citationen_US
dc.description.abstractNoncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to-treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment ( Angrist, Imbens, and Rubin, 1996 , Journal of American Statistical Association 91, 444–472). We propose an extended general location model to estimate the CACE from data with noncompliance and missing data in the outcome and in baseline covariates. Models for both continuous and categorical outcomes and ignorable and latent ignorable ( Frangakis and Rubin, 1999 , Biometrika 86, 365–379) missing-data mechanisms are developed. Inferences for the models are based on the EM algorithm and Bayesian MCMC methods. We present results from simulations that investigate sensitivity to model assumptions and the influence of missing-data mechanism. We also apply the method to the data from a job search intervention for unemployed workers.en_US
dc.format.extent191165 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishingen_US
dc.rightsThe International Biometric Society, 2004en_US
dc.subject.otherCausal Inferenceen_US
dc.subject.otherEM Algorithmen_US
dc.subject.otherGeneral Location Modelen_US
dc.subject.otherMissing Dataen_US
dc.subject.otherNoncomplianceen_US
dc.titleAn Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing Valuesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumBiostatistics Department, SPH, University of Michigan, Ann Arbor, Michigan, USAen_US
dc.contributor.affiliationotherMerck Research Laboratories, West Point, Pennsylvania USAen_US
dc.identifier.pmid15339281en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65568/1/j.0006-341X.2004.00208.x.pdf
dc.identifier.doi10.1111/j.0006-341X.2004.00208.xen_US
dc.identifier.sourceBiometricsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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