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Survival Data Analysis Using Different Statistical Learning Methods
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Mehrnoosh Madadi    , Kiomars Motarjem * |
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Abstract: (710 Views) |
| Due to the volume and complexity of emerging data in survival analysis, it is necessary to use statistical learning methods in this field. These methods can estimate the probability of survival and the effect of various factors on the survival of patients. In this article, the performance of the Cox model as a common model in survival analysis is compared with compensation-based methods such as Cox Ridge and Cox Lasso, as well as statistical learning methods such as random survival forests and neural networks. The simulation results show that in linear conditions, the performance of the models mentioned above is similar to the Cox model. In non-linear conditions, methods such as Cox lasso, random survival forest, and neural networks perform better. Then, these models were evaluated in the analysis of the data of patients with atheromatous, and the results showed that when faced with data with a large number of explanatory variables, statistical learning approaches generally perform better than the classical survival model. |
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| Keywords: Survival Analysis, Statistical Learning, Cox Model, Random Survival Forest, Neural Network. |
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Full-Text [PDF 279 kb]
(563 Downloads)
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Type of Study: Applied |
Subject:
Applied Statistics
Received: 2023/12/16 | Accepted: 2024/05/30 | Published: 2024/12/2
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1. مترجم، ک. (1400)، معرفی یک مدل بقای فضایی با اثرات تصادفی چوله گاوسی و کاربرد آن در تحلیل دادههای بیماری کووید-19، مجله علوم آماری، 15، 567-590 2. Breiman, L. (2001), Random Forests, Machine Learning, 45, 5-32. [ DOI:10.1023/A:1010933404324] 3. Cox, R. D. (1975), Partial Likelihood, Biometrika, 62, 269-276. [ DOI:10.1093/biomet/62.2.269] 4. Davis, R. B. and Anderson, J. R. (1989), Exponential Survival Trees, Statistics in Medicine, 8, 947-961. [ DOI:10.1002/sim.4780080806] [ PMID] 5. Destras, O., Le Beux, S., De Magalhães, F. G., and Nicolescu, G. (2023), Survey on Activation Functions for Optical Neural Networks, ACM Computing Surveys, 56(2), 1-30. [ DOI:10.1145/3607533] 6. Faraggi, D. and Simon R. (1995), A Neural Network Model for Survival Data, Statistics in Medicine, 31, 73-82. [ DOI:10.1002/sim.4780140108] [ PMID] 7. Harrell, F. E., Cali, R. M., Pryor, D. B., Lee, K. L., and Rosati, R. A. (1982), Evaluating the Yield of Medical Tests, Jama, 247, 2543-2546. [ DOI:10.1001/jama.247.18.2543] [ PMID] 8. Harrell, F. E. (2001), Regression Modeling Strategies: with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, Springer, New York. [ DOI:10.1007/978-1-4757-3462-1] 9. Hastie, T. and Tibshirani, R. (1990), Generalized Additive Models, Statistical Sciences, 3, 297-318. 10. Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2, 1-758. [ DOI:10.1007/b94608_8] 11. Henderson, R. (1995), Problems and Prediction in Survival Data Analysis, Statistics in Medicine, 14, 161-184. [ DOI:10.1002/sim.4780140208] [ PMID] 12. Ishwaran, H., Kogalur, U. B., Blackstone, E.H. and Lauer, M. S. (2008), Random Survival Forests, The Annals of Applied Statistics, 2, 841-60. [ DOI:10.1214/08-AOAS169] 13. LeBlanc, M. and Crowley J. (1993), Survival Trees by Goodness of Split, Journal of the American Statistical Association, 88, 457-467. [ DOI:10.1080/01621459.1993.10476296] 14. Liu, L., Yang, F., Fan, Y., Kao, C., Wang, F., Yu, L., and Yu, Z. (2023), An Improved Training Algorithm Based on Ensemble Penalized Cox Regression for Predicting Absolute Cancer Risk, China CDC Weekly, 5(9), 206. [ DOI:10.46234/ccdcw2023.037] [ PMID] [ ] 15. Motarjem, K., (2022), Introduce a Survival Model with Spatial Skew Gaussian Random Effects and its Application in Covid-19 Data Analysis. Journal of Statistical Sciences, 15(2), 567-590. [ DOI:10.52547/jss.15.2.567] 16. Robins, J. M. and Rotnitzky, A. (1992), Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers, Springer, 297-331. [ DOI:10.1007/978-1-4757-1229-2_14] 17. Shimokawa, A., Kawasaki, Y. and Miyaoka, E. (2015), Comparison of Splitting Methods on Survival Tree, The International Journal of Biostatistics, 1, 175-188. [ DOI:10.1515/ijb-2014-0029] [ PMID] 18. Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011), Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, 39, 1-13. [ DOI:10.18637/jss.v039.i05] [ PMID] [ ] 19. Smith, J. (2020),The impact of Technology on Data Analysis, Journal of Data Science, 8(2), 123-135. 20. Smith, J., and Jones, A. (2020), Application of Statistical Learning Techniques in Survival Analysis, Journal of Biostatistics, 10(3), 123-135. 21. Tibshirani, R. (1997), The Lasso Method for Variable Selection in the Cox Model, Statistics in Medicine, 16, 385-395.
https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3 [ DOI:10.1002/(SICI)1097-0258(19970228)16:43.0.CO;2-3] 22. Utkin, L. V., Konstantinov, A. V., Chukanov, V. S., Kots, M. V., Ryabinin, M. A., and Meldo, A. A. (2019), A Weighted Random Survival Forest, Knowledge-Based Systems, 177, 136-144. [ DOI:10.1016/j.knosys.2019.04.015] 23. Wang, D. and Gao, Z., (2023), Distributed Finite-Time Optimization Algorithms with a Modified Newton-Raphson Method. Neurocomputing, 536, 73-79. [ DOI:10.1016/j.neucom.2023.03.027] |
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