Statistical Methods for Survival Data Analysis
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Upgraded to reflect the latest research and software applications on the topic, this new edition continues to provide a comprehensive introduction to the statistical methods for analyzing survival data. It features a wealth of new material, including coverage of marginal and random effect models for analyzing correlated censored or uncensored data as well as multiple comparisons in the comparison of K samples. It also offers expanded coverage of the Cox proportional hazards model; real-world examples focused on survival data in the biomedical sciences; and more.
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 Research
Updated 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 data
Multiple types of two-sample and K-sample comparison analysis
Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models
Expanded coverage of the Cox proportional hazards model
Exercises at the end of each chapter to deepen knowledge of the presented material
Statistical 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.
". . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject." --Statistics in Medical Research
Updated 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 data
Multiple types of two-sample and K-sample comparison analysis
Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models
Expanded coverage of the Cox proportional hazards model
Exercises at the end of each chapter to deepen knowledge of the presented material
Statistical 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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