Survival Analysis

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.

Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.

Features:

• Classical survival analysis techniques for estimating statistical functional and hypotheses testing
• Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.
• Information criteria to facilitate model selection including Akaike, Bayes, and Focused
• Penalized methods consisting of, and elastic net
• Survival trees and ensemble techniques of bagging, boosting, and random survival forests
• A brief exposure of neural networks for survival data
• R program illustration throughout the book

「生存分析」通常處理臨床試驗產生的數據分析。截斷、刪除和缺失數據會帶來分析上的挑戰，統計方法和推論需要新穎和不同的分析方法。估計量和檢驗的統計特性，基本上是漸近的，可以在計數過程框架中適當處理，該框架來自於更大的隨機微積分領域。在過去二十年間，隨著數據生成的爆炸式增長，生存數據也變得巨大。大多數在千禧年之前開發的統計方法，即使在面對複雜的生存數據性質時，也是基於線性方法。非參數非線性方法最好在機器學習學派中設想。本書試圖以簡潔的方式涵蓋所有這些方面。

「生存分析」提供了統計方法和機器學習的綜合結合，用於生存數據的分析。提供此內容的目的是讓讀者了解終身數據分析的機器學習趨勢。

特點:
- 用於估計統計功能和假設檢驗的傳統生存分析技術
- 包括流行的Cox相對風險回歸模型、Aalen的加法風險模型等的回歸方法
- 信息準則以便於模型選擇，包括Akaike、Bayes和Focused
- 懲罰方法，包括Lasso和彈性網絡
- 生存樹和集成技術，如bagging、boosting和隨機生存森林
- 對生存數據的神經網絡的簡要介紹
- 整本書都使用R程式進行示範

Prabhanjan Narayanachar Tattar is working as a Lead Data Scientist at British American Tobacco company, Malaysia. The author has published several books in Statistics: A Course in Statistics with R (Wiley), Statistical Application Development with R and Python, and Hands-on Ensemble Learning with R. He is recipient of the IBS(IR)- GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during PhD. In the year 2021, he has ventured into ction writing and published three novels under the penname of S.B. Akshobhya: The Panipuri Crimes, Finding - A Measure of Her, and Prema Naada Pandita.

H. J. Vaman is a retired professor of Statistics. He taught for over 40 years at Bangaore University and Central University of Rajasthan. He has also served as visiting faculty at Shivaji University, University of Calcutta, Indian Statistical Institute, Bangalore Centre, IIT-Mumbai, and Mangalore University. His main areas of research are sequential decision processes, survival analysis, statistical process control, and modelling in certain health related studies.

Prabhanjan Narayanachar Tattar目前在馬來西亞的英美煙草公司擔任首席數據科學家。該作者在統計學領域發表了多本書籍，包括《統計學與R語言》（Wiley出版）、《使用R和Python進行統計應用開發》以及《實踐集成學習與R語言》。他曾獲得IBS(IR)-GK Shukla青年生物統計學家獎（2005年）和Dr. U.S. Nair青年統計學家獎（2007年）。在攻讀博士期間，他擁有CSIR-UGC的SRF獎學金。2021年，他開始涉足小說創作，以筆名S.B. Akshobhya出版了三本小說：《泡泡餅罪案》、《尋找-她的度量》和《Prema Naada Pandita》。

H. J. Vaman是一位退休的統計學教授。他在班加羅爾大學和拉賈斯坦中央大學教授統計學超過40年。他還曾擔任訪問教授，前往希瓦吉大學、加爾各答大學、印度統計學研究所班加羅爾中心、孟買理工學院和芒格洛爾大學。他的主要研究領域包括序列決策過程、生存分析、統計過程控制以及某些與健康相關的研究模型。