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A global logrank test for adaptive treatment strategies based on observational studies

dc.contributor.authorLi, Zhiguoen_US
dc.contributor.authorValenstein, Marciaen_US
dc.contributor.authorPfeiffer, Paulen_US
dc.contributor.authorGanoczy, Daraen_US
dc.date.accessioned2014-02-11T17:57:02Z
dc.date.available2015-04-01T19:59:06Zen_US
dc.date.issued2014-02-28en_US
dc.identifier.citationLi, Zhiguo; Valenstein, Marcia; Pfeiffer, Paul; Ganoczy, Dara (2014). "A global logrank test for adaptive treatment strategies based on observational studies." Statistics in Medicine 33(5): 760-771.en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/102657
dc.publisherAmerican Psychiatric Publishing, Incen_US
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherWeighted Logrank Testen_US
dc.subject.otherSurvival Outcomeen_US
dc.subject.otherObservational Studyen_US
dc.subject.otherAdaptive Treatment Strategyen_US
dc.titleA global logrank test for adaptive treatment strategies based on observational studiesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/102657/1/sim5987.pdf
dc.identifier.doi10.1002/sim.5987en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceGuo X, Tsiatis AA. A weighted risk set estimator for survival distributions in two‐stage randomization designs with censored survival data. The International Journal of Biostatistics 2005; 1 ( 1 ): 1 – 15. DOI: 10.2202/1557‐4679.1000.en_US
dc.identifier.citedreferenceLi Z, Murphy SA. Sample size formulae for two‐stage randomized trials with survival outcomes. Biometrika 2011; 98 ( 3 ): 503 – 518. DOI: 10.1093/biomet/asr019.en_US
dc.identifier.citedreferenceLunceford JK, Davidian M, Tsiatis AA. Estimation of survival distributions of treatment strategies in two‐stage randomization designs in clinical trials. Biometrics 2002; 58: 48 – 57.en_US
dc.identifier.citedreferenceWahed SA, Tsiatis AA. Semiparametric efficient estimation of survival distribution in two‐stage randomization designs in clinical trials with censored data. Biometrika 2006; 93: 163 – 177. DOI: 10.1093/biomet/93.1.163.en_US
dc.identifier.citedreferencevan der Vaart AW, Wellner JA. Weak Convergence and Empirical Processes. Springer‐Verlag: New York, 1996.en_US
dc.identifier.citedreferenceBechhofer RE, Santner TJ, Goldsman DM. Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley: New York, 1995.en_US
dc.identifier.citedreferenceRoger BN. An Introduction to Copulas. Springer‐Verlag: New York, 1998.en_US
dc.identifier.citedreferenceTsiatis AA. Semiparametric Theory and Missing Data. Springer: New York, 2006.en_US
dc.identifier.citedreferenceKalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. John Wiley: New York, 2002.en_US
dc.identifier.citedreferenceRobins JM. Causal inference from complex longitudinal data. In Latent Variable Modelling and Applications to Causality, Vol. 120, Berkane M (ed.), Lecture Notes in Statistics. Springer: New York, 1997; 69 – 117.en_US
dc.identifier.citedreferenceHolland PW. Statistics and causal inference. Journal of the American Statistical Association 1986; 81: 945 – 960.en_US
dc.identifier.citedreferenceOrellana L, Rotnitzky A, Robins JM. Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: main content. The International Journal of Biostatistics 2010; 6 ( 2 ).en_US
dc.identifier.citedreferenceRobins JM, Orellana L, Rotnitzky A. Estimation and extrapolation of optimal treatment and testing strategies. Statistics in Medicine 2008; 27: 4678 – 4721.en_US
dc.identifier.citedreferenceMurphy SA, van der Laan MJ, Robins JM, CPPRG. Marginal mean models for dynamic regimes. Journal of the American Statistical Association 2001; 96: 1410 – 1423.en_US
dc.identifier.citedreferenceKidwell KM, Wahed SA. Weighted log‐rank statistic to compare shared‐path adaptive treatment strategies. Biostatistics 2013; 14 ( 2 ): 299 – 312. DOI: 10.1093/biostatistics/kxs042.en_US
dc.identifier.citedreferenceMiyahara S, Wahed SA. Weighted Kaplan–Meier estimators for two‐stage treatment regimes. Statistics in Medicine 2010; 29: 2581 – 2591. DOI: 10.1002/sim.4020.en_US
dc.identifier.citedreferenceLokhnygina Y, Helterbrand JD. Cox regression methods for two‐stage randomization designs. Biometrics 2007; 63: 422 – 428. DOI: 10.1111/j.1541‐0420.2006.00707.x.en_US
dc.identifier.citedreferenceRobins JM. A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect. Computers and Mathematics with Applications 1986; 14: 1393 – 1512.en_US
dc.identifier.citedreferenceLavori PW, Dawson R, Roth AJ. Flexible treatment strategies in chronic disease: clinical and research implications. Biological Psychiatry 2000; 48: 605 – 614. DOI: 10.1016/S0006‐3223(00)00946‐X.en_US
dc.identifier.citedreferenceMurphy SA. An experimental design for the development of adaptive treatment strategies. Statistics in Medicine 2005; 24: 1455 – 1481. DOI: 10.1002/sim.2022.en_US
dc.identifier.citedreferenceMurphy SA, Lynch KG, Oslin D, McKay JR, TenHave T. Developing adaptive treatment strategies in substance abuse research. Drug and Alcohol Dependence 2007; 88 ( 2 ): S24 – S30. DOI: 10.1016/j.drugalcdep.2006.09.008.en_US
dc.identifier.citedreferenceRush AJ, Fava M, Wisniewski SR, Lavori PW, Trivedi MH, Sackeim HA, Thase ME, Nierenberg AA, Quitkin FM, Kashner TM. Sequenced treatment alternatives to relieve depression (STAR*D): rationale and design. Controlled Clinical Trials 2004; 25: 119 – 142. DOI: 10.1016/S0197‐2456(03)00112‐0.en_US
dc.identifier.citedreferenceRobins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11 ( 5 ): 550 – 560.en_US
dc.identifier.citedreferenceHernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV‐positive men. Epidemiology 2000; 11 ( 5 ): 561 – 570.en_US
dc.identifier.citedreferenceRobins JM, Rotnitzky A, Zhao LP. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 1994; 89: 846 – 866.en_US
dc.identifier.citedreferenceBreslow NE, Wellner JA. Weighted likelihood for semiparametric models and two‐phase stratified samples, with application to Cox regression. Scandinavian Journal of Statistics 2007; 34: 86 – 102.en_US
dc.identifier.citedreferenceLi Z, Gilbert P, Nan B. Weighted likelihood method for grouped survival data in case–cohort studies with application to HIV vaccine trials. Biometrics 2008; 64: 1247 – 1255.en_US
dc.identifier.citedreferenceLi Z, Nan B. Relative risk regression for current status data in case–cohort studies. Canadian Journal of Statistics 2011; 39: 557 – 577.en_US
dc.identifier.citedreferenceOetting AI, Levy JA, Weiss RD, Murphy SA. Statistical methodology for a SMART design in the development of adaptive treatment strategies. In Causality and Psychopathology: Finding the Determinants of Disorders and their Cures, Shrout PE (ed.). American Psychiatric Publishing, Inc: Arlington VA, 2007.en_US
dc.identifier.citedreferenceFeng W, Wahed SA. A supremum log rank test for comparing adaptive treatment strategies and corresponding sample size formula. Biometrika 2008; 95 ( 3 ): 695 – 707. DOI: 10.1093/biomet/asn025.en_US
dc.identifier.citedreferenceFeng W, Wahed SA. Sample size for two‐stage studies with maintenance therapy. Statistics in Medicine 2009; 28: 2028 – 2041. DOI: 10.1002/sim.3593.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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