Statistical Methods for Survival Data Analysis.
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生存数据分析的统计方法
- 研究问题
- 所给教材介绍旨在说明如何分析生存数据，包括相关及删失观测、组间比较和回归建模，但没有提出单一研究问题。
- 方法与思路
- 介绍列出边际与随机效应模型、加速失效时间模型、Cox 比例风险模型及多组比较，并以实例、软件应用和习题辅助学习。
- 材料陈述的贡献
- 材料称第四版更新并扩展了生存分析方法的介绍，补充相关数据和回归主题；这是教材内容说明，不能视为原创研究贡献。
- 阅读关注点
- 应核对实际书籍版本、目录与软件说明，重点检查删失机制、相关性处理及各类生存回归模型的假设与适用范围。
- 阅读边界
- 依据版本未注明的索引文本，内容含第三版评价与第四版宣传；无法确认其与所给 DOI 条目的对应关系或实际版本。
原文摘要
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.