The DOI matches its Crossref record.
Statistical Methods for Survival Data Analysis.
Abstract
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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Citations
Cited by
(1998) · New England Journal of Medicine · doi:10.1056/nejm199811053391902 · Cited by 5,261
Gage, B. F., Waterman, A. D., Shannon, W., Boechler, M., Rich, M. W., & Radford, M. J. (2001) · JAMA · doi:10.1001/jama.285.22.2864 · Cited by 4,832
Ohkubo, Y., Kishikawa, H., Araki, E., Miyata, T., Isami, S., Motoyoshi, S., Kojima, Y., Furuyoshi, N., & Shichiri, M. (1995) · Diabetes Research and Clinical Practice · doi:10.1016/0168-8227(95)01064-k · Cited by 3,079
Log-normal distributions across the sciences: keys and clues
LIMPERT, E., STAHEL, W. A., & ABBT, M. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2 · Cited by 2,621
Naylor, M. D., Brooten, D., Campbell, R., Jacobsen, B. S., Mezey, M. D., Pauly, M. V., & Schwartz, J. S. (1999) · JAMA · doi:10.1001/jama.281.7.613 · Cited by 1,831
Metadata
- Authors
- Lachenbruch, P. A., & Lee, E. T.
- Venue
- Journal of the American Statistical Association
- Publisher
- JSTOR
- Published
- 1993-03
- Volume
- 88
- Issue
- 421
- Pages
- 380
- ISSN
- 0162-1459 (ISSN-L)
- OpenAlex
- W2171515720
- Cited by
- 2,845
- References
- 0
- FWCI
- 133.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lachenbruch, P. A., & Lee, E. T. (1993). Statistical Methods for Survival Data Analysis. Journal of the American Statistical Association. https://doi.org/10.2307/2290742
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.