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Introducing Survival and Event History Analysis

Introducing Survival and Event History Analysis

December 2010 | 300 pages | SAGE Publications Ltd



Introducing Survival Analysis and Event History Analysis is an accessible, practical and comprehensive guide for researchers and students 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 and resources for both students and instructors, Introducing Survival Analysis and Event History Analysis allows researchers to quickly master these advanced statistical techniques. This book is written from the perspective of the 'user', making it suitable as both a self-learning tool and graduate-level textbook.


Introducing Survival Analysis and Event History Analysis covers the most up-to-date innovations in the field, including advancements in the assessment of model fit, frailty and recurrent event models, discrete-time methods, competing and multistate models and sequence analysis. Practical instructions are also included, focusing on the statistical program R and Stata, enabling readers to replicate the examples described in the text.


This book comes with a glossary, a range of practical and user-friendly examples, cases and exercises.

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
May 16, 2018

Provides a great introduction to Survival analysis.

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

Sample Materials & Chapters

Chapter 1

For instructors

Please contact your Academic Consultant to check inspection copy availability for your course.

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