Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome.

• Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome
• Uses consistent notation throughout the book for the different techniques presented
• Explains in which situation each of these models should be used, and how they are linked to specific research questions
• Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians
• Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets

This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

生存資料分析是統計學中一個非常廣泛的領域，包含了在各種應用中使用的多種方法，尤其在醫學研究中。在過去的二十年中，已經開發了幾種擴展的「傳統」生存模型，以應對實踐中常遇到的特定情況。本書旨在將最常用的擴展方法集結在一個參考資料中，例如脆弱性模型（用於未觀察到的異質性或集群數據）、治癒模型（當部分人口不會發生感興趣的事件時）、競爭風險模型（在不同類型的事件情況下）以及用於時間至事件結束點和縱向結果的聯合生存模型。

特點：
- 提供不同高級生存模型的最新方法，包括脆弱性模型、治癒模型、競爭風險模型和聯合模型（用於縱向和生存結果）
- 在整本書中使用一致的符號表示不同的技術
- 解釋了每個模型應該在哪種情況下使用，以及它們如何與特定的研究問題相關聯
- 侧重於對模型的理解、實施和解釋，適合碩士生和應用統計學家
- 提供現有的R套件和SAS程序或宏的參考資料，並在真實數據集上演示主要套件的使用

本書主要針對應用統計學家和統計學及生物統計學研究生。對於對傳統生存分析的主要擴展感興趣的方法學研究人員，本書也可以作為入門參考資料。

Catherine Legrand is Professor in Statistics and Biostatistics at the Institute of Statistics, Biostatistics, and Actuarial Sciences (ISBA-LIDAM) of the Université Catholique de Louvain (UCLouvain, Belgium). She obtained a Master Degree in Mathematics from the Université Libre de Bruxelles (ULB, Belgium) in 1998. She worked for 7 years at the European Organization for Research and Treatment of Cancer (EORTC, Brussels) and became the primary statistician of the EORTC Lung Cancer Group. She was also a member of the EORTC Treatment Outcome Research Group, the Elderly Task Force, and coordinator of the EORTC Independent Data Monitoring Committee. In parallel, she completed a PhD in 2005 at the Center for Statistics, Hasselt University, in the field of survival analysis (frailty models). Early 2006, she started working as biometrician at Merck Sharp & Dohme (MSD) where she was involved in the design and analysis of clinical trials in respiratory diseases. In September 2007, she joined the Université Catholique de Louvain (UCLouvain). Her area of research includes survival data analysis, design and analysis of clinical trials and analysis of medical data. Along with these professional experiences, she co-authored more than 80 papers in peer-reviewed clinical and statistical journals.

Catherine Legrand是比利時魯汶天主教大學統計學、生物統計學和精算學研究所（ISBA-LIDAM）的統計學和生物統計學教授。她於1998年從布魯塞爾自由大學（ULB）獲得數學碩士學位。她在歐洲癌症研究和治療組織（EORTC）工作了7年，成為EORTC肺癌小組的首席統計師。她還是EORTC治療結果研究小組的成員，老年人工作組的成員，以及EORTC獨立數據監測委員會的協調人。同時，她在2005年在哈塞爾特大學統計中心完成了博士學位，專攻生存分析（脆弱模型）。2006年初，她開始在默克夏普和杜邁（MSD）擔任生物統計學家，參與呼吸系統疾病臨床試驗的設計和分析。2007年9月，她加入了魯汶天主教大學。她的研究領域包括生存數據分析、臨床試驗的設計和分析以及醫學數據分析。除了這些專業經驗外，她還與他人合著了80多篇同行評審的臨床和統計學期刊論文。