View Item 

Show simple item record

Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication

dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorShen, Jinchengen_US
dc.contributor.authorKennedy, Edward H.en_US
dc.contributor.authorWang, Luen_US
dc.contributor.authorSchaubel, Douglas E.en_US
dc.date.accessioned2014-01-08T20:34:41Z
dc.date.available2015-03-02T14:35:34Zen_US
dc.date.issued2014-01-30en_US
dc.identifier.citationTaylor, Jeremy M. G.; Shen, Jincheng; Kennedy, Edward H.; Wang, Lu; Schaubel, Douglas E. (2014). "Comparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indication." Statistics in Medicine 33(2): 257-274.en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/102122
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherProstate Canceren_US
dc.subject.otherCausal Effecten_US
dc.subject.otherProportional Hazards Modelen_US
dc.subject.otherTime‐Dependent Confounderen_US
dc.subject.otherTreatment by Indicationen_US
dc.titleComparison of methods for estimating the effect of salvage therapy in prostate cancer when treatment is given by indicationen_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/2027.42/102122/1/sim5890.pdf
dc.identifier.doi10.1002/sim.5890en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceXiao Y, Abrahamowicz M, Moodie EEM. Accuracy of conventional and marginal structural Cox model estimators: a simulation study. International Journal of Biostatistics 2010; 6 ( 2 ). (Article 13).en_US
dc.identifier.citedreferenceYoung JG, Hernán MA, Picciotto S, Robins JM. Relation between three classes of structural models for the effect of a time‐varying exposure on survival. Lifetime Data Analysis 2010; 16 ( 1 ): 71 – 84.en_US
dc.identifier.citedreferenceWestreich D, Cole SR, Schisterman EF, Platt RW. A simulation study of finite‐sample properties of marginal structural Cox proportional hazards models. Statistics in Medicine 2012; 31 ( 19 ): 2098 – 2109.en_US
dc.identifier.citedreferenceTaubmann SL, Robins JM, Mittleman MA, Hernán MA. Intervening on risk factors for coronary heart disease: an application of the parametric g‐formula. International Journal of Epidemiology 2009; 38: 1599 – 1611.en_US
dc.identifier.citedreferenceAbadie A, Imbens GW. On the failure of the bootstrap for matching estimators. Econometrica 2008; 76: 1537 – 1557.en_US
dc.identifier.citedreferenceFewell Z, Hernán MA, Wolfe F, Tilling K, Choi H, Sterne JAC. Controlling for time‐dependent confounding using marginal structural models. The Stata Journal 2004; 4: 402 – 420.en_US
dc.identifier.citedreferenceKaufman JS. Marginalia: comparing adjusted effect measures. Epidemiology 2010; 21: 490 – 493.en_US
dc.identifier.citedreferenceKang JDY, Schafer JL. Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science 2007; 22: 523 – 539.en_US
dc.identifier.citedreferenceScharfstein DO, Rotnitzky A, Robins JM. Adjusting for non‐ignorable drop‐out using semi‐parametric nonresponse models. JASA 1999; 94: 1096‐1120. Article 15.en_US
dc.identifier.citedreferenceCole SR, Hernán MA. Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology 2008; 168: 656 – 664.en_US
dc.identifier.citedreferenceHansen BB. The prognostic analogue of the propensity score. Biometrika 2008; 95: 481 – 488.en_US
dc.identifier.citedreferenceTaylor JMG, Park Y, Ankerst DP, Proust‐Lima C, Williams S, Kestin L, Bae K, Pickles T, Sandler H. Real‐time individual predictions of prostate cancer recurrence using joint models. Biometrics 2013. DOI: 10.1111/j.1541‐0420.2012.01823.x. [Epub ahead of print].en_US
dc.identifier.citedreferenceLok JJ, Gill RD, van der Vaart AW, Robins JM. Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models. Statistica Neerlandica 2004; 58 ( 3 ): 271 – 295.en_US
dc.identifier.citedreferenceProust‐Lima C, Taylor JMG, Williams SG, Ankerst DP, Liu N, Kestin LL, Bae K, Sandler HM. Determinants of change in prostate‐specific antigen over time and its association with recurrence after external beam radiation therapy for prostate cancer in five large cohorts. International Journal of Radiation Oncology Biology Physics 782; 72 ( 3 ).en_US
dc.identifier.citedreferenceCommenges D, Gegout‐Petit A. A general dynamical statistical model with causal interpretation. Journal of the Royal Statistical Society: Series B 2009; 71 ( 3 ): 719 – 736.en_US
dc.identifier.citedreferenceAalen OO, Roysland K, Gran JM, Ledergerber B. Causality, mediation and time: a dynamic viewpoint. Journal of the Royal Statistical Society: Series A 2012; 174 ( 4 ): 831 – 862.en_US
dc.identifier.citedreferenceAalen OO, Frigessi A. What can statistics contribute to a causal understanding? Scandinavian Journal of Statistics 2007; 34 ( 1 ): 155 – 168.en_US
dc.identifier.citedreferenceErtefaie A, Stephens DA. Comparing approaches to causal inference for longitudinal data: inverse probability weighting versus propensity scores. International Journal of Biostatistics 2010; 6 ( 2 ). (Article 14).en_US
dc.identifier.citedreferenceZagars GK, von Eschenbach AC. Prostate‐specific antigen: an important marker for prostate cancer treated by external beam radiation therapy. Cancer 2007; 112 ( 2 ): 307 – 314.en_US
dc.identifier.citedreferenceRobins JM. Marginal structural models. Proceedings of the American Statistical Association, Section on Bayesian Statistical Science, 1997; 1 – 10.en_US
dc.identifier.citedreferenceRobins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11 ( 5 ): 550 – 560.en_US
dc.identifier.citedreferenceHernán 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.citedreferenceCole SR, Hernán MA, Robins JM, et al. Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. American Journal of Epidemiology 2003; 158: 687 – 694.en_US
dc.identifier.citedreferenceHernán MA, Robins JM. Estimating causal effects from epidemiologic data. Journal of Epidemiol Community Health 2006; 60: 578 – 586.en_US
dc.identifier.citedreferencevan der Laan MJ, Petersen ML, Joffe MM. History‐adjusted marginal structural models and statically‐optimal dynamic treatment regimens. The International Journal of Biostatistics 2005; 1 ( 1 ): 10 – 20. (Article 4).en_US
dc.identifier.citedreferencePeterson ML, Deeks SG, Martin JN, van der Laan MJ. History‐adjusted marginal structural models for estimating time‐varying effect modification. American Journal of Epidemiology 2007; 166 ( 9 ): 985 – 993.en_US
dc.identifier.citedreferenceKennedy EH, Taylor JMG, Schaubel DE, Williams SG. The effect of salvage therapy on survival in a longitudinal study with treatment by indication. Statistics in Medicine 2010; 29 ( 25 ): 2569 – 2580.en_US
dc.identifier.citedreferenceSchaubel DE, Wolfe RA, Port FK. A sequential stratification method for estimating the effect of a time‐dependent experimental treatment in observational studies. Biometrics 2006; 62: 910 – 917.en_US
dc.identifier.citedreferenceSchaubel DE, Wolfe RA, Sima CS, Merion RM. Estimating the effect of a time‐dependent treatment by levels of an internal time‐dependent covariate: application to the contrast between liver wait‐list and posttransplant mortality. Journal of the American Statistical Association 2009; 104 ( 485 ): 49 – 59.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim5890.pdf
Size:
299.8KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.