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Survival Analysis

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 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

GBP 99.99
1

Handbook of Survival Analysis

Handbook of Survival Analysis

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field the handbook focuses on advances in survival analysis techniques covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

GBP 66.99
1

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research Fourth Edition describes the analysis of survival data illustrated using a wide range of examples from biomedical research. Written in a non-technical style it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data Cox regression and parametric modelling the book covers many more advanced techniques including interval-censoring frailty modelling competing risks analysis of multiple events and dependent censoring. This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model joint models for longitudinal and survival data and modern methods for the analysis of interval-censored survival data. Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author’s many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher’s website Modelling Survival Data in Medical Research Fourth Edition is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres research scientists and clinicians who are analysing their own data and students following undergraduate or postgraduate courses in survival analysis.

GBP 74.99
1

Advanced Survival Models

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. Features 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.

GBP 42.99
1