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Introducing Survival and Event History Analysis

Introducing Survival and Event History Analysis

February 2011 | 300 pages | SAGE Publications Ltd
Introducing Survival and Event History Analysis is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences.

Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics.

Engaging, easy to read, functional, and packed with enlightening examples, "hands-on" exercises, conversations with key scholars and resources for both students and instructors, Introducing Survival and Event History Analysis allows researchers to quickly master advanced statistical techniques. This unique book is written from the perspective of the "user," making it suitable as both a self-learning tool and graduate-level textbook.

Introducing Survival and Event History Analysis covers up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events, and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.

The Fundamentals of Survival and Event History Analysis
Introduction: What Is Survival and Event History Analysis?  
Key Concepts and Terminology  
Censoring and Truncation  
Mathematical Expression and Relation of Basic Statistical Functions  
How Do the Survivor, Density and Hazard Function Relate?  
Why Use Survival and Event History Analysis?  
Overview of Survival and Event History Models  
Using R and Other Computer Programs for Survival and Event History Analysis
Introduction: Computer Programs for Survival and Event History Analysis  
Conducting Serious Data Analysis: Life Lessons  
Why Use R?  
Downloading R on Your Personal Computer  
Add-On Packages  
Running R  
Determining and Setting your Working Directory  
Help and Documentation  
Importing Data Into R  
Working With Data: Opening and Accessing Variables from a Data Frame  
Saving Output as File, Workspace and History and Quitting R  
Your First Session: Using the Survival Package and Exploring Data Via Descriptive Statistics and Graphs
Your First Session Using the 'Survival' Package In F  
Loading and Examining the Survival Package and Rcmdrplugin.Survival Plug-In  
Opening and Examining Data  
The Surv Object: Packaging the 'Survival Variable'  
Basic Descriptive Statistics  
Descriptive Data Exploration with Graphs  
Data and Data Reconstruction
Introduction: Why Discuss Data and Data Preparation?  
Sources of Event History Data  
Single-Episode Data for Single Transition Analyses  
Multi-Episode Data for Recurrent Event and Frailty Analyses  
Subject-(Person)-Period Data for Discrete-Time Hazard Models  
The Counting Process and Episode Splitting  
A Note on Dates  
Non-Parametric Methods: Estimating and Comparing Survival Curves Using the Kaplan-Meier Estimator
The Kaplan-Meier Estimator  
Producing Kaplan-Meier Estimates  
Plotting the Kaplan-Meier Survival Curve  
Testing Differences Between Two Groups Using Survdiff  
Stratifying the Analysis by a Covariate  
The Cox Proportional-Hazards Regression
Introduction: Why is The Cox Model So Popular?  
The Cox Regression Model  
Estimating and Interpreting The Cox Model with Fixed Covariates  
The Cox Regression Model with Time-Varying Covariates  
Parametric Models
Introduction: What are Parametric Models and Why Use Them?  
Proportional Hazards (Ph) Versus Accelerated Failure Time (Aft) Models  
The Path to Choosing a Model  
Estimating and Interpreting Parametric Survival Models  
Exponential and Piecewise Constant Exponential Model  
Weibull Model  
Log-Logistic and Log-Normal Models  
Additional Parametric Models  
Finding the Best Fitting Parametric Model  
Model Building and Diagnostics
Model Building and Selection of Covariates  
Assessing the Overall Goodness of Fit of Your Model  
What is Residual Analysis?  
Testing Overall Model Adequacy: Cox-Snell Residuals  
Testing the Proportional Hazards Assumption: Schoenfeld Residuals  
Checking For Influential Observations: Score Residuals (Dfbeta Statistics)  
Assessing Nonlinearity: Martingale Residual and Component-Plus-Residual Plots  
Correlated and Discrete-Time Survival Data: Frailty, Recurrent Events and Discrete-Time Models
Shared Frailty: Modeling Recurrent Events and Clustering In Groups  
Other Frailty Models: Unshared, Nested, Joint and Additive Models  
Estimating Frailty Models in R  
Example of Frailty Model Estimation and Interpretation  
Discrete-Time and Count Models  
Multiple Events and Entire Histories: Competing Risk, Multistate Models and Sequence Analysis
Competing Risk Models  
Multistate Models  
Sequence Analysis: Modeling Entire Histories  
Appendix : Datasets Used in this Book  

This book is very useful for researchers and students

in different scientific areas – social sciences and humanities, medicine, in

general every science where studies measuring time changes in variables are

conducted...As the author explains, this book is written from the

perspective of an absolute beginner – comprehensible and with a lot of examples

in the text, tables and graphs. It goes beyond an introductory textbook on this

topic, because it presents not only non-parametric models, semi-parametric

models, parametric models, model-building and model diagnostics, but it is focused also on some more recent techniques like frailty and recurrent event

history models, discrete-time models, multistate models, competing risk

analysis and sequence analysis...Everyone who would like to start with Survival and

Event History analysis or to get more knowledge of Survival and Event History

analysis could do this by reading this book
Stanislava Yordanova Stoyanova

Excellent basic resource for students at the graduate level. The real plus is the reference to both R and Stata, which is a pragmatic approach given the current state of affairs when it comes to software.

Dr Mathew Creighton
School of Sociology, University College Dublin
March 7, 2018

Provides a great introduction to Survival analysis.

Dr Madhurima Sarkar
Communications Dept, Florida State University
October 5, 2011

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ISBN: 9781848601024
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