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On Bayesian methods of exploring qualitative interactions for targeted treatment

dc.contributor.authorChen, Weien_US
dc.contributor.authorGhosh, Debashisen_US
dc.contributor.authorRaghunathan, Trivellore E.en_US
dc.contributor.authorNorkin, Maximen_US
dc.contributor.authorSargent, Daniel J.en_US
dc.contributor.authorBepler, Gerolden_US
dc.date.accessioned2012-12-11T17:37:24Z
dc.date.available2014-02-03T16:21:44Zen_US
dc.date.issued2012-12-10en_US
dc.identifier.citationChen, Wei; Ghosh, Debashis; Raghunathan, Trivellore E.; Norkin, Maxim; Sargent, Daniel J.; Bepler, Gerold (2012). "On Bayesian methods of exploring qualitative interactions for targeted treatment." Statistics in Medicine 31(28): 3693-3707. <http://hdl.handle.net/2027.42/94482>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/94482
dc.publisherJohn Wiley & Sons, Ltden_US
dc.subject.otherInteractionen_US
dc.subject.otherClinical Trialen_US
dc.subject.otherPrognostic Markeren_US
dc.subject.otherPredictive Markeren_US
dc.subject.otherSubgroupen_US
dc.titleOn Bayesian methods of exploring qualitative interactions for targeted treatmenten_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.identifier.pmid22733620en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/94482/1/sim5429.pdf
dc.identifier.doi10.1002/sim.5429en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceNeter J, Kutner MH, Nachtsheim CJ, Wasserman W. Applied Linear Statistical Models. McGraw‐Hill: New York, 1996.en_US
dc.identifier.citedreferenceReynolds C, Obasaju C, Schell MJ, Li X, Zheng Z, Boulware D, Caton JR, Demarco LC, O'Rourke MA, Shaw Wright G, Boehm KA, Asmar L, Bromund J, Peng G, Monberg MJ, Bepler G. Randomized phase III trial of gemcitabine‐based chemotherapy with in situ RRM1 and ERCC1 protein levels for response prediction in Non‐Small‐Cell lung cancer. Journal of Clinical Oncology 2009; 27 ( 34 ): 5808 – 5815.en_US
dc.identifier.citedreferenceCamp RL, Chung GG, Rimm DL. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nature Medicine 2002; 8: 1323 – 1328.en_US
dc.identifier.citedreferenceSoria JC. ERCC1‐tailored chemotherapy in lung cancer: the first prospective randomized trial. Journal of Clinical Oncology 2007; 25 ( 19 ): 2648 – 2649. DOI: 10.1200/JCO.2007.11.3167.en_US
dc.identifier.citedreferenceOlaussen KA, Dunant A, Fouret P, Brambilla E, André F, Haddad V, Taranchon E, Filipits M, Pirker R, Popper HH, Stahel R, Sabatier L, Pignon J‐P, Tursz T, Le Chevalier T, Soria J‐C. Dna repair by ercc1 in non‐small‐cell lung cancer and cisplatin‐based adjuvant chemotherapy. New England Journal of Medicine 2006; 355 ( 10 ): 983 – 991. DOI: 10.1056/NEJMoa060570. Available from: http://www.nejm.org/doi/full/10.1056/NEJMoa060570.en_US
dc.identifier.citedreferenceGoldberg RM, Sargent DJ, Morton RF, Fuchs CS, Ramanathan RK, Williamson SK, Findlay BP, Pitot HC, Alberts SR. A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. Journal of Clinical Oncology 2004; 22: 23 – 30.en_US
dc.identifier.citedreferenceHarrell FE, Lee KL, Mark DB. Multivariate prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996; 15: 361 – 387.en_US
dc.identifier.citedreferenceChen W, Ghosh D, Raghunathan TE, Sargent DJ. Bayesian variable selection with joint modeling of categorical and survival outcomes: an application to individualizing chemotherapy treatment in advanced colorectal cancer. Biometrics 2009; 65: 1030 – 1040. DOI: 10.1111/j.1541‐0420.2008.01181.x.en_US
dc.identifier.citedreferenceMüller P, Parmigiani G, Robert C, Rousseau J. Optimal sample size for multiple testing: the case of gene expression microarrays. Journal of the American Statistical Association 2004; 99: 990 – 1001.en_US
dc.identifier.citedreferenceGelfand AE, Smith AFM. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association 1990; 85: 398 – 409.en_US
dc.identifier.citedreferenceGeman S, Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE‐PAMI 1984; 6: 721 – 741.en_US
dc.identifier.citedreferenceGilks WR, Thomas A, Spiegelhalter DJ. A language and program for complex Bayesian modelling. The Statistician 1994; 43: 169 – 178.en_US
dc.identifier.citedreferenceLindley DV. Making Decisions, 2nd edn. Wiley: New York, 1971.en_US
dc.identifier.citedreferenceMacQueen JB. Some methods for classification and analysis of multivariate observations. In Proceedings of 5‐th Berkeley Symposium on Mathematical Statistics and Probability, Vol.  1. University of California Press: Berkeley, CA, 1967; 281 – 297.en_US
dc.identifier.citedreferencePeduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 1996; 49: 1373 – 1379.en_US
dc.identifier.citedreferenceChipman H. Bayesian variable selection with related predictors. The Canadian Journal of Statistics 1996; 24: 17 – 36.en_US
dc.identifier.citedreferenceKalbfleisch JD. Nonparametric Bayesian analysis of survival time data. Journal of the Royal Statistical Society, Series B 1978; 40: 214 – 221.en_US
dc.identifier.citedreferenceClayton DG. Bayesian analysis of frailty models. Technical Report, Medical Research Council Biostatistics Unit, Cambridge, U.K, 1994.en_US
dc.identifier.citedreferenceAndersen PK, Gill RD. Cox's regression model for counting processes: a large sample study. The Annals of Statistics 1982; 10: 1100 – 1120.en_US
dc.identifier.citedreferenceMok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin‐paclitaxel in pulmonary adenocarcinoma. New England Journal of Medicine 2009; 361: 947 – 957.en_US
dc.identifier.citedreferenceKarapetis CS, Khambata‐Ford S, Jonker DJ, O'Callaghan CJ, Tu D, Tebbutt NC, Simes RJ, Chalchal H, Shapiro JD, Robitaille S, Price TJ, Shepherd L, Au HJ, Langer C, Moore MJ, Zalcberg JR. K‐ras mutations and benefit from cetuximab in advanced colorectal cancer. New England Journal of Medicine 2008; 359: 1757 – 1765.en_US
dc.identifier.citedreferencePeto R. Statistical aspects of cancer trials. In The Treatment of Cancer, Halnan KE (ed.). Chapman & Hall: London, 1982.en_US
dc.identifier.citedreferenceGail M, Simon R. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 1985; 41: 361 – 372.en_US
dc.identifier.citedreferenceDixon DO, Simon R. Bayesian subset analysis. Biometrics 1991; 47: 871 – 881.en_US
dc.identifier.citedreferenceGunter L, Zhu J, Murphy SA. Variable selection for qualitative interactions. Statistical Methodology 2011; 8: 420 – 55. DOI: 10.1016/j.stamet.2009.05.003.en_US
dc.identifier.citedreferenceBayman EÖ, Chaloner K, Cowles MK. Detecting qualitative interaction: a Bayesian approach. Statistics in Medicine 2010; 29: 455 – 463. DOI: 10.1002/sim.3787.en_US
dc.identifier.citedreferenceGeorge EI, McCulloch RE. Variable selection via Gibbs sampling. Journal of the American Statistical Association 1993; 88: 881 – 889.en_US
dc.identifier.citedreferenceGhosh D, Chen W, Raghunathan TE. The false discovery rate: a variable selection perspective. Journal of Statistical Planning and Inference 2006; 136: 2668 – 2684.en_US
dc.identifier.citedreferenceChen W, Ghosh D, Raghunathan TE, Sargent DJ. A false‐discovery‐rate‐based loss framework for selection of interactions. Statistics in Medicine 2008; 27: 2004 – 2021. DOI: 10.1002/sim.3118.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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