Introducing Survival and Event History Analysis

by Melinda Mills

• ISBN13: 9781848601024
• ISBN10: 1848601026
• Format: Paperback
• Copyright: 2011-02-23
• Publisher: SAGE Publications Ltd

Summary

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.Key features of this book include:- Data preparation and censored data- Comparing survival curves and Kaplan-Meier estimation- Model development, selection and diagnostic methods- Inclusion of time-varying covariates- Competing risks- Modeling strategies for unobserved heterogeneity, frailty models- Recent advances in event history techniques - including multilevel event history models and the study of recurrent events- Exercises and summaries for each chapter- Practical integration of examples and a blueprint for the entire research path- A wide range of supportive materials are available to download at the companion website, including the example data sets and various programming files for different computer programs (R, STATA, SPSS), plus study and teaching materialMelinda Mills (PhD Demography) is a Professor of Sociology at the University of Groningen in the Netherlands where she teaches courses in survival and event history analysis. She is the Editor of International Sociology (journal of the International Sociological Association), member of the Life History Analysis group of the European Science Foundation's (ESF) Quantitative Methods in the Social Sciences program and a Steering Committee Member of the ESF TransEurope Research Project.

Table of Contents

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

Exercises

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

Exercises

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

Exercises

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

Exercises

Non-Parametric Methods: Estimating and Comparing Survival Curves Using the Kaplan-Meier Estimator

Introduction

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

Exercises

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

Exercises

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

Exercises

Model Building and Diagnostics

Introduction

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

Exercises

Correlated and Discrete-Time Survival Data: Frailty, Recurrent Events and Discrete-Time Models

Introduction

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

Exercises

Multiple Events and Entire Histories: Competing Risk, Multistate Models and Sequence Analysis

Introduction

Competing Risk Models

Multistate Models

Sequence Analysis: Modeling Entire Histories

Exercises

Appendix : Datasets Used in this Book

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