Melinda Mills

Introducing Survival and Event History Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: SAGE Publications
• Seitenzahl: 300
• Erscheinungstermin: 30. Dezember 2010
• Englisch
• ISBN-13: 9781446209820
• Artikelnr.: 38406717

Inhaltsangabe

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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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

Rezensionen

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
Methodspace