Prabhanjan Narayanachar Tattar - 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 the 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 the complex nature of survival data.

Nonparametric nonlinear methods, commonly employed in statistical inference for analysis of survival data are best envisaged in the Machine Learning setting. This book attempts to cover all these aspects in a concise way.

Survival Analysisoffers an integrated blend of statistical methods and machine learning useful in the analysis of survival data. A special feature of the offering is to give an exposure to the machine learning trends for lifetime data analysis.

Features:

• Classical survival analysis techniques for estimating relevant statistical functions and hypotheses testing

• Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model and related diagnostics.

• Information criteria to facilitate model selection including Akaike, Bayes, and Focused

• Penalization methods covering l 1 , l 2 , and SCAD

• 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

Prabhanjan Narayanachar Tattarhas industrial experience with Dell International Services, Ford Motor Company, and British American Tobacco. The author has published several books in Statistics: A Course in Statistics with R (J. Wiley), Statistical Application Development with R and Python, and Hands-on Ensemble Learning with R. He is a recipient of the IBS(IR)-GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007). He held Senior Research Fellowship of CSIR-UGC during PhD. In the year 2021, he has ventured into fiction writing and published three novels under the penname S.B. Akshobhya.

H. J. Vamanis a retired professor of statistics. He has taught the subject for over 40 years at Bangalore 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 modeling in certain health-related studies.

First edition published 2023

by CRC Press

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CRC Press is an imprint of Taylor & Francis Group, LLC

2023 H J Vaman & Prabhanjan Narayanachar Tattar

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ISBN: 978-0-367-03037-7 (hbk)