Biomedical Survival

Topics

  • Cox model

  • survival analysis 

  • survival time and life table

  • Kaplan-Meier curves moments methods

  • Stanton Glanz (Primer in biostatistics)

  • Partial AUC Estimation and Regression

  • Laryngeal Carconoma (LC) and Ocular Melanoma (OM)

  • Evaluation of survival analysis methods

Tools

Unsorted

  • Prediction of Community-Acquired Pneumonia Using Artificial Neural Networks
    P. S. Heckerling, B. S. Gerber, T. G. Tape, and R. S. Wigton
    Med Decis Making, March 1, 2003; 23(2): 112 - 121. 
    http://mdm.sagepub.com/cgi/reprint/23/2/112.pdf 
     

  • A neural-Bayesian approach to survival analysis (1999)
    Bart Bakker and Tom Heskes
    http://citeseer.ist.psu.edu/bakker99neuralbayesian.html 
     

  • Computational Methods in Survival Analysis
    Toshinari Kamakura 
    http://www.quantlet.com/mdstat/scripts/csa/html/node195.html  
     

  • SVM, Survival Analysis, fMRI, Network Introsion Detection 
    http://www.quantlet.com/mdstat/scripts/csa/html/csahtml.html 
     

  • eBook PDF
    http://www.xplore-stat.de/ebooks/ebooks.html  
     

  • Dairy Science 
    Search: http://jds.fass.org/search.dtl  
     

  • A novel neural network-based survival analysis model
    Antonio Eleuteri, Roberto Tagliaferri, Leopoldo Milanob, Sabino De Placido and Michele De Laurentiis
    Neural Networks Volume 16, Issues 5-6 , June-July 2003, Pages 855-864 Advances in Neural Networks Research: IJCNN '03
     
    Abstract: A feedforward neural network architecture aimed at survival probability estimation is presented which generalizes the standard, usually linear, models described in literature. The network builds an approximation to the survival probability of a system at a given time, conditional on the system features. The resulting model is described in a hierarchical Bayesian framework. Experiments with synthetic and real world data compare the performance of this model with the commonly used standard ones.
     

  • Are increasing 5-year survival rates evidence of success against cancer?
    http://knox.dartmouth.edu/fellowships/downloads/WelchHG_JAMA.pdf  
     

  • breast cancer survival analysis
    http://pubgeneserver.uio.no/~ehovig/Jenssen2.pdf  
     

  • Five Year Survival Analysis of Patients with Clinical Stages I and IIA Breast Cancer who Received Initial Treatment at North Carolina Hospitals
    Fatma Simsek, MPH
    http://www.schs.state.nc.us/SCHS/pdf/CHIS123.pdf 
     

  • Proportional hazards and additive regression analysis of survival for severe breast cancer (2004)
    Anna Torner (Mathematical Statistics Stockholm University)
    http://www.math.su.se/matstat/reports/serieb/2004/rep3/report.pdf 
     

  • Additive regression analysis of survival in severe breast cancer
    Anna Torner
    Abstract: Survival data from a study of 52 patients with advanced breast cancer was investigated using the Aalen's additive model and the Cox proportional hazards model. The information gained from fitting of the two models is similar in some respects but also quite different in others. Both procedures resulted in the same covariates selected to remain in the model. The Cox model yield easily interpreted estimates of the covariates effects, but the assumption of proportional hazard is necessary to make these estimates valid. The additive model and the plots of the cumulative regressions functions give an appealing understanding of how the hazard profile is distributed. The presentation will focus on the additive model and results from the Cox model will only be referenced for comparative comments.
     

  • Bioinformatics and Biomedical Comp. Chairs: Francesco Masulli & Roberto Tagliaferri (ENNS Special Session)
     

  • An Information Geometric Approach to Survival Analysis and Feature Selection by Neural Networks
    Antonio Eleuteri, Roberto Tagliaferri, Leopoldo Milano, Fausto Acernese and Michele De Laurentiis
     

  • HLA Typing Using a Fuzzy Approach
    Giovanni Battista Ferrara, Francesco Masulli and Stefano Rovetta
     

  • Neuro-Fuzzy Analysis of Dermatological Images (slides)
    Ciro Castiello, Giovanna Castellano and Anna Maria Fanelli
     

  • Suggested themes and papers for the Data Analysis seminar 
    http://www.cs.helsinki.fi/u/jmjsinkk/op/cda/papers.html
      
     

  • Publications: http://tahoe.inesc-id.pt/pt/indicadores/Publicacoes/list_all.php3?numero=21&firstyear=1980 
     

  • Pepe Biometrics 2003
     

  • Artificial Neural Networks In Survival Analysis: Flexible Tools For Building Prognostic Models
    Elia Biganzoli, University of Milan, Italy
     

  • Permutation Tests For The Comparison Of Survival After Matching 
    M. G. Valsecchi, S. Galimberti, University of Milano-Bicocca, Italy
     

  • Extensions Of The Cox Ph Model: Reduced-Rank Hazards Regression Model
    A. Perperoglou and H.Van Houwelingen, Leiden University Medical Center, Netherlands
     

Conferences

Groups

Researchers

Papers

  • Lisboa, P.J.G. and Wong, H.
    Are neural networks best used to help logistic regression? An example from breast cancer survival analysis.
    Proc. International Joint Conference on Neural Networks, Washington.
    D.C., paper 577, 4-19 July, 2001 
     

  • Are neural networks best used to help logistic regression? An example from breast cancer survival analysis.
    Lisboa, P.J.G. and Wong, H.
    Proc. International Joint Conference on Neural Networks, Washington. D.C., paper 577, 4-19 July, 2001 
     

  • Computational Methods in Survival Analysis 
    Toshinari Kamakura 
    http://www.case.hu-berlin.de/index_html/Publikationen/papers/papersKatalog/29_tk.pdf
     
     

  • Neural Network Survival Analysis for Personal Loan Data
    Bart Baesens1, Tony Van Gestel2, Maria Stepanova3, Jan Vanthienen
    K.U.Leuven, Dept. of Applied Economic Sciences, Naamsestraat 69, B-3000 Leuven, Belgium
    {Bart.Baesens; Jan.Vanthienen}@econ.kuleuven.ac.be
    http://www.crc.ems.ed.ac.uk/conference/presentations/baesens-etal-paper.pdf  
     

  • Patient survival estimation with multiple attributes: adaptation of Coxís regression to give an individualís point prediction
    Ann E. Smith, Sarabjot S. Anand.
    IDAMAP 2000
    http://ai.ijs.si/Branax/idamap-2000_AcceptedPapers/Smith.pdf
     
     

  • A novel neural network-based survival analysis model
    Antonio Eleuteri , Roberto Tagliaferri , Leopoldo Milano , Sabino De Placido , Michele De Laurentiis
    Neural Networks, v.16 n.5-6, p.855-864, June 2003 
     

  • Neural networks as statistical methods in survival analysis (1998)
    Ripley, B. D. and Ripley, R. M. (1998)
    In `Artificial Neural Networks: Prospects for Medicine eds R. Dybowski and V. Gant, Landes Biosciences Publishers.
     

  • Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering
    Wenting Zhou, Weichen Wu, Nathan Palmer, Emily Mower, Noah Daniels, Lenore Cowen, Anselm Blumer 
    http://www.cra.org/Activities/craw/dmp/awards/2003/Mower/paper.pdf
     
     

  • Survival analysis: A neural-Bayesian approach. 
    Bakker, B.J., Kappen, H.J., & Heskes, T.M. (2000)
    In Proceedings Artificial Neural Networks in Medicine and Biology (pp. 162-167). Berlin: Springer. 
     

  • Neural Networks as Statistical Methods in Survival Analysis
    B.D. Ripley and R.M. Ripley 
    http://www.stats.ox.ac.uk/pub/bdr/NNSM.pdf 
     

  • Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem (2004)
    Azzam F G Taktak1, Anthony C Fisher1 and Bertil E Damato
    Phys. Med. Biol. 49 ( 2004) p 87-98 
     

  • Parametric mixture model for analysing relative survival of patients with multiple cancers
    Hein® avaara S, Hakulinen T.
     

  • MODELLING SURVIVAL OF PATIENTS WITH MULTIPLE CANCERS
    Sirpa Heinavaara (Master of Social Sciences)
    http://ethesis.helsinki.fi/julkaisut/val/tilas/vk/heinavaara/modellin.pdf 
     

IEEE search

  • An artificial neural network based feature evaluation index for the assessment of clinical factors in breast cancer survival analysis
    Seker, H.; Odetayo, M.O.; Petrovic, D.; Naguib, R.N.G.; Bartoli, C.; Alasio, L.; Lakshmi, M.S.; Sherbet, G.V.; Hinton, O.R.;
    Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on , Volume: 2 , 12-15 May 2002 Pages:1211 - 1215 vol.2
     

  • A neural-Bayesian approach to survival analysis
    Bakker, B.; Heskes, T.;
    Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) , Volume: 2 , 7-10 Sept. 1999
    Pages:832 - 837 vol.2
     

  • A fuzzy measurement-based assessment of breast cancer prognostic markers
    Seker, H.; Odetayo, M.; Petrovic, D.; Naguib, R.N.G.; Bartoli, C.; Alasio, L.; Lakshmi, M.S.; Sherbet, G.V.;
    Information Technology Applications in Biomedicine, 2000. Proceedings. 2000 IEEE EMBS International Conference on , 9-10 Nov. 2000
    Pages:174 - 178
     

  • Are neural networks best used to help logistic regression? An example from breast cancer survival analysis
    Lisboa, P.J.G.; Wong, H.;
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on , Volume: 4 , 15-19 July 2001
    Pages:2472 - 2477 vol.4
     

  • Survival analysis and maintenance policies for a series system, with highly censored data
    Reineke, D.M.; Pohl, E.A.; Murdock, W.P.;
    Reliability and Maintainability Symposium, 1998. Proceedings., Annual , 19-22 Jan. 1998
    Pages:182 - 188
     

  • Survival analysis and neural networks
    Eleuteri, A.; Tagliaferri, R.; Milano, L.; Sansonell, G.; D'Agostino, D.; De Placido, S.; De Laurentiis, M.;
    Neural Networks, 2003. Proceedings of the International Joint Conference on , Volume: 4 , July 20 - 24, 2003
    Pages:2631 - 2636
     

  • Towards a theory of lung cancer's survival analysis based on data foundry project
    Haahong Zhu; Qilun Zheng; Hong Peng;
    Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. The 2002 International Conference on , June 16-19, 2002
    Pages:211 - 211
     

  • Prognostic value of histology and lymph node status in bilharziasis-bladder cancer: outcome prediction using neural networks
    Ji, W.; Naguib, R.N.G.; Petrovic, D.; Gaura, E.; Ghoneim, M.A.;
    Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE , Volume: 4 , 25-28 Oct. 2001
    Pages:3870 - 3873 vol.4
     

Unsorted

  • On the use of artificial neural networks for the analysis of survival data
    Brown, S.F.; Branford, A.J.; Moran, W.;
    Neural Networks, IEEE Transactions on ,Volume: 8 , Issue: 5 , Sept. 1997  Pages:1071 - 1077
     

  • A large sample study of the life table and product limit estimates under random censorship
    N. Breslow and J. Crowley, ď
    Ann. Statist., vol. 2, pp. 437-453, 1974.
     

  • Survival analysis using artificial neural networks
    P. L. Choong, C. J. S. deSilva, J. Taran, P. Heenan, and H. Dawkins
    Proc. 1st Australia and New Zealand Conf. Intell. Inform. Syst., pp. 283-287, 1993.
     

  • Analysis of Survival Data
    D. R. Cox and D. Oakes
    London: Chapman and Hall, 1984.
     

  • Artificial neural networks and breast cancer prognosis
    C. J. S. deSilva, P. L. Choong, and Y. Attikiouzel
    Australian Comput. J., vol. 26, pp. 78-81, 1994.
     

  • Introduction to the Theory of Neural Computation
    J. Hertz, A. Krogh, and R. G. Palmer
    Redwood City, CA: Addison-Wesley, 1991.
     

  • The Statistical Analysis of Failure Time Data
    J. D.Kalbfleisch and R. L. Prentice
    New York: Wiley, 1980.
      

  • Nonparametric estimation from incomplete observations
    E. L.Kaplan and P.Meier
    J. Amer. Statist. Association, vol. 53, pp. 457-481, 1958.
     

Thesis

Presentation

Interesting conferences on the topic 

2006 - SPMC / SoCCE / UoP