15 research outputs found

    A flowgraph model for bladder carcinoma

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    Background: Superficial bladder cancer has been the subject of numerous studies for many years, but the evolution of the disease still remains not well understood. After the tumor has been surgically removed, it may reappear at a similar level of malignancy or progress to a higher level. The process may be reasonably modeled by means of a Markov process. However, in order to more completely model the evolution of the disease, this approach is insufficient. The semi-Markov framework allows a more realistic approach, but calculations become frequently intractable. In this context, flowgraph models provide an efficient approach to successfully manage the evolution of superficial bladder carcinoma. Our aim is to test this methodology in this particular case. Results: We have built a successful model for a simple but representative case. Conclusion: The flowgraph approach is suitable for modeling of superficial bladder cancer.Rubio Navarro, G.; García Mora, MB.; Santamaria Navarro, C.; Pontones Moreno, JL. (2014). A flowgraph model for bladder carcinoma. Theoretical Biology and Medical Modelling. 11(1):1-11. doi:10.1186/1742-4682-11-S1-S3S111111van Rhijn BW, Burger M, Lotan Y, Solsona E, Stief CG, Sylvester RJ, Witjes JA, Zlotta AR: Recurrence and progression of disease in non-muscle-invasive bladder cancer: from epidemiology to treatment strategy. Eur Urol. 2009, 56: 430-42. 10.1016/j.eururo.2009.06.028.Sylvester RJ, van der Meijden AP, Oosterlinck W, Witjes JA, Bouffioux C, Denis L, Newling DW, Kurth K: Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials. 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Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability December 1, 2008 vol 222 no 4. 2008, 667-664.Huzurbazar A: Multistate Models, Flowgraph Models, and Semi-Markov Processes. Communications in Statistics - Theory and Methods. 2004, 33: 457-474. 10.1081/STA-120028678.Huzurbazar A: Flowgraph Models for Multistate Time-To-Event Data. 2005, New York: WileyMullen KM, van Stokkum IHM: nnls: The Lawson-Hanson algorithm for non-negative least squares (NNLS). 2012, [R package version 1.4], http://CRAN.R-project.org/package=nnlsAbate J, Whitt W: The Fourier-Series Method For Inverting Transforms Of Probability Distributions. Queueing Syst. 1992, 5-88.Collins DH, Huzurbazar AV: Prognostic models based on statistical flowgraphs. 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PhD thesis. 2012, Universitat de ValènciaTeam RDC: R: A Language and Environment for Statistical Computing. 2010, R Foundation for Statistical Computing, Vienna, Austria,Goulet V, Dutang C, Maechler M, Firth D, Shapira M, Stadelmann M, expm-developers@listsR-forgeR-projectorg: expm: Matrix exponential. 2011, [R package version 0.98-5], http://CRAN.R-project.org/package=expmBates D, Maechler M: Matrix: Sparse and Dense Matrix Classes and Methods. 2011, R package version 1.0-1.Therneau T: survival: Survival analysis, including penalised likelihood. 2011, original Splus: R port by Thomas Lumley, [R package version 2.36-10], http://CRAN.R-project.org/package=survivalJackson CH: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software. 2011, 38 (8): 1-29. http://www.jstatsoft.org/v38/i08

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