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Survival probabilities for HIV infected patients through semi-Markov processes

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Barbu V.S., Limnios N. (2008):Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications: Their Use in Reliability and DNA Analysis. Lecture Notes in Statistics N° 191, Springer, New York. DOI: 10.1007/978-0-387-73173-5.10.1007/978-0-387-73173-5Search in Google Scholar

Brookmeyer R., Gail M.H. (1994): AIDS Epidemiology: a Quantitative Approach. Oxford University Press, New York.Search in Google Scholar

Centres for Disease Control and Prevention (1993): Revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recommendations and Reports, 41 N° RR-17: 1-19Search in Google Scholar

Corradi G., Janssen J., Manca R. (2004):Numerical treatment of homogeneous semi- Markov processes in transient case-a straightforward approach. Methodology and Computing in Applied Probability 6: 233-246.10.1023/B:MCAP.0000017715.28371.85Search in Google Scholar

D’Amico G., Di Biase G., Janssen J., Manca R. (2011):HIV Evolution: A Quantification of the Effects Due to Age and to Medical Progress. Informatica 22 (1): 27-42.10.15388/Informatica.2011.312Search in Google Scholar

Davidov O. (1999): The steady state probabilities for a regenerative semi-Markov processes with application to prevention and screening. Applied Stochastic Models and Data Analysis 15: 55-63.10.1002/(SICI)1099-0747(199903)15:1<55::AID-ASM358>3.0.CO;2-4Search in Google Scholar

Davidov O., Zelen M. (2000): Designing cancer prevention trials: a stochastic approach., Statistics in Medicine 19: 1983-1995.10.1002/1097-0258(20000815)19:15<1983::AID-SIM534>3.0.CO;2-ESearch in Google Scholar

Di Biase G., D’Amico G., Di Girolamo A., Janssen J., Iacobelli S., Tinari N., Manca R. (2007a): Homogeneous semi-Markov model for predicting the HIV disease evolution: a case study. Far Edst. J. Math. Sci. (FJMS) 27: 89-109.10.1155/2007/65636Search in Google Scholar

Di Biase G., D’Amico G., Di Girolamo A., Janssen J., Iacobelli S., Tinari N., Manca R. (2007b):A Stochastic Model for the HIV/AIDS Dynamic Evolution. Mathematical problem in Engineering Art. ID 65636, 14 pages. DOI: 10.1155/2007/65636.10.1155/2007/65636Search in Google Scholar

Di Biase G., D’Amico G., Janssen J., Manca R. (2009): Patient’s Age Depending HIV/AIDS Evolution Analysis by means of a Non Homogeneous Semi-Markov Model. Advances and Applications in Statistics 11: 199-215. ISSN: 0972-3617.Search in Google Scholar

Fischl M.A., Reichmann D.D., Grieco M.H. et al. (1987): The efficacy of azidothymidine (AZT) in the treatment of patients with AIDS and AIDS related complex. A double blind placebo-controlled trial. New England Journal of Medicine 317: 185-191.10.1056/NEJM198707233170401Search in Google Scholar

Foucher Y., Mathieu E., Saint-Pierre P., Durand J.F., Daurès J.P. (2005): A semi- Markov model based on generalized Weibull distribution with an illustration for HIV disease. Biometrical Journal 47: 825-833.10.1002/bimj.200410170Search in Google Scholar

Foucher Y. (2007): Modèles semi-markoviens: Application à l'analyse de l'évolution de pathologies chroniques.Doctoral dissertation, Université de Montpellier 1Search in Google Scholar

Goedert J.J. (1990): Prognostic markers for AIDS. Annals of Epidemiology 1: 129-139.10.1016/1047-2797(90)90004-CSearch in Google Scholar

Goshu A.T., Dessie Z.G. (2013): Modelling Progression of HIV/AIDS Disease Stages Using Semi-Markov Processes. Journal of Data Science 11: 269-280.10.6339/JDS.2013.11(2).1136Search in Google Scholar

Howard R.A. (1971a): Dynamic Probabilistic Systems, Markov Models. John Wiley & Sons Vol. 1, New York.Search in Google Scholar

Howard R.A. (1971b): Dynamic Probabilistic Systems, Semi-Markov and Decision Processes. John Wiley & Sons Vol. 2, New York.Search in Google Scholar

Iosifescu Manu A. (1972): Non homogeneous semi-Markov processes, Stud. Lere. Mat. 24: 529-533.Search in Google Scholar

Jaffe H.W., Lifson A.R. (1988): Acquisition and transmission of HIV, Infectious Diseases Clinic of North America 2: 299-306.10.1016/S0891-5520(20)30184-7Search in Google Scholar

Janssen J., Manca R. (2006): Applied Semi-Markov Processes. Springer, New York.Search in Google Scholar

Joly P., Commenges D. (1999): A penalized likelihood approach for a progressive three-state model with censored and truncated data: application to AIDS. Biometrics 55: 887-890.10.1111/j.0006-341X.1999.00887.xSearch in Google Scholar

Lagakos S.W., Sommer C.J., Zelen M. (1978): Semi-Markov models for partially censored Data. Biometrika 65: 311-317.10.1093/biomet/65.2.311Search in Google Scholar

Levy P. (1954): Processus semi-markoviens. Proceedings of the International Congress of Mathematicians 3: 416-426, Erven P. Noordhoff N.V., Groningen, The Netherlands.Search in Google Scholar

Levy J.A. (1993): Pathogenesis of human immunodeficiency virus infection. Microbiological Reviews 57: 183-289.10.1128/mr.57.1.183-289.19933729058464405Search in Google Scholar

Longini I.M., Clark J., Gardner W.S., Brundage J. (1991):The dynamics of CD4+ T lymphocyte decline in HIV infected individuals: A Markov modelling approach. Journal of Acquired Immunodeficiency Syndromes 4: 1141-1147.Search in Google Scholar

Marshall A.H., Shaw B., McClean S.I. (2007): Estimating the costs for a group of geriatric patients using the Coxian phase-type distribution. Statistics in Medicine 26: 2716-2729.10.1002/sim.272817072824Search in Google Scholar

Satten G.A., Sternberg M.R. (1999): Fitting semi-Markov models to interval-censored data with unknown initiation times. Biometrics 55: 507-513.10.1111/j.0006-341X.1999.00507.xSearch in Google Scholar

Smith W.L. (1955): Regenerative stochastic processes. Proceedings of the Royal Society of London Series A. 232: 6-31.Search in Google Scholar

Sternberg M.R., Satten S.A. (1999): Discrete-time nonparametric estimation for semi- Markov models of chain-of-events data subject to interval-censoring and truncation. Biometrics 55: 514-522.10.1111/j.0006-341X.1999.00514.xSearch in Google Scholar

Sweeting M.J., De Angelis D., Aalen O.O. (2005): Bayesian back-calculation using a multi-State model with application to HIV. Statistics in Medicine 24: 3991-4007.10.1002/sim.243216320278Search in Google Scholar

Tsiatis A.A., Dafni U., De Gruttola V. et al. (1992): The relationship of CD4 counts over time to survival of patients with AIDS: Is CD4 a good surrogated marker? Jewell N., Dietz K. and Farewell V (eds.), AIDS Epidemiology: Methodological Issues, Boston, Birkhauser: 257-274.Search in Google Scholar

UNAIDS/WHO AIDS Epidemic Update December 2006 (2006): available at http://www.unaids.org/en/HIV_data/epi2006/default.asp.Search in Google Scholar

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Argomenti della rivista:
Life Sciences, Bioinformatics, other, Mathematics, Probability and Statistics, Applied Mathematics