- Authors:
- Elisa T. Lee,
- John Wenyu Wang
Praise for the Third Edition. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject. Statistics in Medical ResearchUpdated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences.Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes:Marginal and random effect models for analyzing correlated censored or uncensored dataMultiple types of two-sample and K-sample comparison analysisUpdated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time modelsExpanded coverage of the Cox proportional hazards modelExercises at the end of each chapter to deepen knowledge of the presented materialStatistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
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- Manco G, Ritacco E and Barbieri N (2020). A Factorization Approach for Survival Analysis on Diffusion Networks, IEEE Transactions on Knowledge and Data Engineering, 33:1, (1-13), Online publication date: 1-Jan-2021.
- Gu W, Zhang Z, Xie X and He Y (2021). An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18:2, (500-511), Online publication date: 1-Mar-2021.
- Duan W, Khan Z, Gulistan M, Khurshid A and Stevic Z (2021). Neutrosophic Exponential Distribution, Complexity, 2021, Online publication date: 1-Jan-2021.
- Muse A, Mwalili S, Ngesa O, Almalki S, Abd-Elmougod G and Khalil A (2021). Bayesian and Classical Inference for the Generalized Log-Logistic Distribution with Applications to Survival Data, Computational Intelligence and Neuroscience, 2021, Online publication date: 1-Jan-2021.
- Chen L, Shao K, Long X and Wang L (2020). Multi-task regression learning for survival analysis via prior information guided transductive matrix completion, Frontiers of Computer Science: Selected Publications from Chinese Universities, 14:5, Online publication date: 3-Jan-2020.
Wang P, Li Y and Reddy C (2019). Machine Learning for Survival Analysis, ACM Computing Surveys, 51:6, (1-36), Online publication date: 30-Nov-2019.- Du D, Zhang J, Zhou Z, Si X, Hu C and Huang D (2018). Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations, Journal of Control Science and Engineering, 2018, Online publication date: 1-Jan-2018.
- Yang G, Cai Y and Reddy C Spatio-temporal check-in time prediction with recurrent neural network based survival analysis Proceedings of the 27th International Joint Conference on Artificial Intelligence, (2976-2983)
- Nogueras R and Cotta C (2018). Analyzing self-? island-based memetic algorithms in heterogeneous unstable environments, International Journal of High Performance Computing Applications, 32:5, (676-692), Online publication date: 1-Sep-2018.
- Martin K, Zamor P and Golden E Assessing the Remaining Life of Liquid Reserve Batteries 2018 Annual Reliability and Maintainability Symposium (RAMS), (1-4)
- Shafiq M, Atif M and Viertl R (2018). Beyond precision, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 22:22, (7355-7365), Online publication date: 1-Nov-2018.
- Martínez-Flórez G, Bolfarine H and Gómez H (2017). The Log-Linear Birnbaum-Saunders Power Model, Methodology and Computing in Applied Probability, 19:3, (913-933), Online publication date: 1-Sep-2017.
- Li Y, Rakesh V and Reddy C Project Success Prediction in Crowdfunding Environments Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, (247-256)
- Li Y, Wang J, Ye J and Reddy C A Multi-Task Learning Formulation for Survival Analysis Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1715-1724)
- Isaksen A Computer-aided game design Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, (3998-3999)
- Ameri S, Fard M, Chinnam R and Reddy C Survival Analysis based Framework for Early Prediction of Student Dropouts Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, (903-912)
- Kurashima T, Iwata T, Takaya N and Sawada H Probabilistic latent network visualization Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, (1236-1245)
- Fard N and Sadeghzadeh K Complex data classification in weighted accelerated failure time model 2016 Annual Reliability and Maintainability Symposium (RAMS), (1-6)
Index Terms
- Statistical Methods for Survival Data Analysis
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