Non-linear survival analysis using neural networks

Statistics in Medicine

Volumn 23, Issue 5, 2004, Pages 825-842

  Ripley, Ruth M ^a   Harris, Adrian L ^b   Tarassenko, Lionel ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b CHURCHILL HOSPITAL   (United Kingdom)

Author keywords

Breast cancer; Cross Validation hazard; Neural network; Non linear; Survival

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CANCER PATIENT; CANCER RECURRENCE; CANCER SURVIVAL; COMPUTER PROGRAM; CONTROLLED STUDY; COVARIANCE; HAZARD ASSESSMENT; HUMAN; INTERMETHOD COMPARISON; NONLINEAR REGRESSION ANALYSIS; PREDICTION; STATISTICAL ANALYSIS; STATISTICAL MODEL; SURVIVAL TIME;

BREAST NEOPLASMS; CARCINOMA, DUCTAL, BREAST; FEMALE; HUMANS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; SURVIVAL ANALYSIS;

EID: 1442350707     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.1655     Document Type: Article

References (18)

1. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. Wiley: New York, 1980.
2. Ripley BD. Pattern Recognition and Neural Networks. Cambridge University Press: Cambridge, 1996.
3. Mathieson MJ. Ordinal Models and Predictive Methods in Pattern Recognition. D.Phil. Thesis, University of Oxford, 1998.
4. Ripley BD, Ripley RM. Neural networks as statistical methods in survival analysis. In Clinical Applications of Artificial Neural Networks, Dybowski R, Gant V (eds). Cambridge University Press: Cambridge, 2001.
5. Faraggi D, Simon R. A neural network model for survival data. Statistics in Medicine 1995; 14:73-82.
6. McCullagh P, Nelder JA. Generalized Linear Models (2nd edn). Chapman & Hall: London, 1989.
7. Gasparini G, Gullick WJ, Bevilacqua P, Sainsbury JRC, Meli S, Boracchi P, Testolin A, La Malfa G, Pozza F. Human breast cancer: prognostic significance of the c-erbB-2 oncoprotein compared with epidermal growth factor receptor, DNA ploidy, and conventional pathologic features. Journal of Clinical Oncology 1992; 10: 686-695.
8. Gasparini G, Barbereschi M, Doglioni C, Palma PD, Mauri FA, Boracchi P, Bevilacqua P, Caffo O, Morelli L, Verderio P, Pezzella F, Harris AL. Expression of bcl-2 protein predicts efficacy of adjuvant treatments in operable node-positive breast cancer. Clinical Cancer Research 1995; 1:189-198.
9. Peto R. Contribution to the discussion of the paper by D. R. Cox. Journal of the Royal Statistical Society B 1972; 34:205-207.
10. Ripley RM. Neural network models for breast cancer prognosis. D.Phil. Thesis, University of Oxford, 1998; Available at http://www.stats.ox.ac.uk/~ruth/.
11. Breslow NE. Contribution to the discussion of the paper by D. R. Cox. Journal of the Royal Statistical Society Series B 1972; 34:216-217.
12. Tukey JW. The problem of multiple comparisons. Unpublished report.
13. Veronesi U, Marubini E, Del Vecchio M, Manzari A, Andreola S, Greco M, Luini A, Merson M, Saccozzi R, Rilke F, Salvadori B. Local recurrences and distant metastases after conservative breast cancer treatments: partly independent events. Journal of the National Cancer Institute 1995; 87:19-27.
14. Demicheli R, Abbattista A, Miceli R, Valagussa P, Bonadonna G. Time distribution of the recurrence risk for breast cancer patients undergoing mastectomy: further support about the concept of tumor dormancy. Breast Cancer Research and Treatment 1996; 41:177-185.
15. Ramsay JO. Pspline package of S-PLUS functions; Available at http://lib.stat.cmu.edu/S/pspline.
16. Therneau TM, Atkinson EJ. An introduction to recursive partitioning using the RPART routines. Technical Report, Mayo Foundation, 1997; Available with software at http://lib.stat.cmu.edu/S/rpart.
17. Venables WN, Ripley BD. Modern Applied Statistics with S (4th edn). Springer: New York, 2002.
18. Mathieson MJ. Ordinal models for neural networks. In Neural Networks in Financial Engineering, Refences A-PN, Abu-Mostafa Y, Moody J, Weigend A. (eds), Proceedings of the Third International Conference on Neural Networks in the Capital Markets. World Scientific: Singapore, 1996, 523-536.