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Joint modeling compliance and outcome for causal analysis in longitudinal studies

dc.contributor.authorGao, Xinen_US
dc.contributor.authorBrown, Gregory K.en_US
dc.contributor.authorElliott, Michael R.en_US
dc.date.accessioned2014-08-06T16:49:43Z
dc.date.availableWITHHELD_14_MONTHSen_US
dc.date.available2014-08-06T16:49:43Z
dc.date.issued2014-09-10en_US
dc.identifier.citationGao, Xin; Brown, Gregory K.; Elliott, Michael R. (2014). "Joint modeling compliance and outcome for causal analysis in longitudinal studies." Statistics in Medicine 33(20): 3453-3465.en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/108032
dc.publisherCRC pressen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherNoncomplianceen_US
dc.subject.otherPrincipal Stratificationen_US
dc.subject.otherCausal Inferenceen_US
dc.subject.otherPotential Outcomeen_US
dc.titleJoint modeling compliance and outcome for causal analysis in longitudinal studiesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/108032/1/sim5811.pdf
dc.identifier.doi10.1002/sim.5811en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceEfron B, Feldman D. Compliance as an explanatory variable in clinical trials. Journal of the American Statistical Association 1991; 86 ( 413 ): 9 – 17.en_US
dc.identifier.citedreferenceRosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70: 41 – 55.en_US
dc.identifier.citedreferenceRubin DB. Comment: Neyman (1923) and causal inference in experiments and observational studies. Statistical Science 1990; 5 ( 4 ): 472 – 480.en_US
dc.identifier.citedreferenceFrangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics 2002; 58 ( 1 ): 21 – 29.en_US
dc.identifier.citedreferenceImbens GW, Rubin DB. Bayesian inference for causal effects in randomized experiments with noncompliance. The Annals of Statistics 1997; 25 ( 1 ): 305 – 327.en_US
dc.identifier.citedreferenceCheng J, Small D. Bounds on causal effects in three‐arm trials with non‐compliance. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2006; 68 ( 5 ): 815 – 836.en_US
dc.identifier.citedreferenceYau LHY, Little RJ. Inference for the complier‐average causal effect from longitudinal data subject to noncompliance and missing data, with application to a job training assessment for the unemployed. Journal of the American Statistical Association 2001; 96 ( 456 ): 1232 – 1244.en_US
dc.identifier.citedreferenceLin JY, Have TRT, Elliott MR. Longitudinal nested compliance class model in the presence of time‐varying noncompliance. Journal of the American Statistical Association 2008; 103 ( 482 ): 462 – 473.en_US
dc.identifier.citedreferenceLin JY, Have TRT, Elliott MR. Nested Markov compliance class model in the presence of time‐varying noncompliance. Biometrics 2009; 65 ( 2 ): 505 – 513.en_US
dc.identifier.citedreferenceFrangakis CE, Brookmeyer RS, Varadhan R, Safaeian M, Vlahov D, Strathdee SA. Methodology for evaluating a partially controlled longitudinal treatment using principal stratification, with application to a needle exchange program. Journal of the American Statistical Association 2004; 99 ( 465 ): 239 – 249.en_US
dc.identifier.citedreferenceRubin DB. Bayesian inference for causal effects: the role of randomization. The Annals of Statistics 1978; 6 ( 1 ): 34 – 58.en_US
dc.identifier.citedreferenceRubin DB. Randomization analysis of experimental data: the Fisher randomization test comment. Journal of the American Statistical Association 1980; 75 ( 371 ): 591 – 593.en_US
dc.identifier.citedreferenceAngrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 1996; 91 ( 434 ): 444 – 455.en_US
dc.identifier.citedreferenceElliott MR, Joffe MM, Chen Z. A potential outcomes approach to developmental toxicity analyses. Biometrics 2006; 62 ( 2 ): 352 – 360.en_US
dc.identifier.citedreferenceElliott M, Raghunathan T, Li Y. Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes. Biostatistics 2010; 11: 353 – 372.en_US
dc.identifier.citedreferenceTanner MA, Wong WH. An application of imputation to an estimation problem in grouped lifetime analysis. Technometrics 1987; 29 ( 1 ): 23 – 32.en_US
dc.identifier.citedreferenceGelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. CRC press: Boca Raton, Florida, 2004.en_US
dc.identifier.citedreferenceBrown GK, Ten Have T, Henriques GR, Xie SX, Hollander JE, Beck AT. Cognitive therapy for the prevention of suicide attempts: a randomized controlled trial. Journal of the American Medical Association 2005; 294 ( 5 ): 563 – 570.en_US
dc.identifier.citedreferenceJin H, Rubin DB. Principal stratification for causal inference with extended partial compliance. Journal of the American Statistical Association 2008; 103 ( 481 ): 101 – 111.en_US
dc.identifier.citedreferenceLong Q, Little RJA, Lin X. Estimating causal effects in trials involving multitreatment arms subject to non‐compliance: a Bayesian framework. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2010; 59 ( 3 ): 513 – 531.en_US
dc.identifier.citedreferenceRoy J, Hogan JW, Marcus BH. Principal stratification with predictors for compliance for randomized trials with 2 active treatments. Biostatistics 2008; 9 ( 2 ): 277 – 289.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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