An Introduction to Survival Analysis Using Stata, Third Edition
Stata Press – 2010 – 412 pages
An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject.
This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata’s new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression.
After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata’s survival analysis routines.
Praise for the Second Edition
Unlike some glorified manuals available in the market, this book is a genuine text for an introductory course in survival analysis using Stata. This book is also an excellent supplement for a graduate-level survival analysis course as well as a reference book for a data analyst dealing with survival data. The book presents the essential models, formulas, background, and relevant references in a compact and adequate manner, and then continues to present the relevant tools, their implementation, and explanation of outputs. …
—The American Statistician, November 2010, Vol. 64, No. 4
The Problem of Survival Analysis
Linking the three approaches
Describing the Distribution of Failure Times
The survivor and hazard functions
The quantile function
Interpreting the cumulative hazard and hazard rate
Means and medians
Analysis time (time at risk)
Censoring and Truncation
Recording Survival Data
The desired format
Example: Wide-form snapshot data
A short lesson on dates
Purposes of the stset command
Syntax of the stset command
Look at stset’s output
List some of your data
Perhaps use stfill
Example: Hip fracture data
Inadequacies of standard univariate methods
The Kaplan–Meier estimator
The Nelson–Aalen estimator
Estimating the hazard function
Estimating mean and median survival times
Tests of hypothesis
The Cox Proportional Hazards Model
Cox models with shared frailty
Cox models with survey data
Cox model with missing data–multiple imputation
Model Building Using stcox
Modeling group effects: fixed-effects, random-effects, stratification, and clustering
The Cox Model: Diagnostics
Testing the proportional-hazards assumption
Residuals and diagnostic measures
Classes of parametric models
A Survey of Parametric Regression Models in Stata
The exponential model
Gompertz regression (PH metric)
Lognormal regression (AFT metric)
Loglogistic regression (AFT metric)
Generalized gamma regression (AFT metric)
Choosing among parametric models
Postestimation Commands for Parametric Models
Use of predict after streg
Generalizing the Parametric Regression Model
Using the ancillary() option
Power and Sample-Size Determination for Survival Analysis
Estimating sample size
Accounting for withdrawal and accrual of subjects
Estimating power and effect size
Tabulating or graphing results
Cumulative incidence functions
Mario Cleves is a professor of pediatrics at the University of Arkansas for Medical Sciences and a senior biostatistician at the Arkansas Center for Birth Defects Research and Prevention.
William Gould is the president and head of development at StataCorp.
Roberto Gutierrez is the director of statistics at StataCorp.
Yulia Marchenko is a senior statistician at StataCorp.
All are authors of Stata statistical software, in particular, Stata’s widely used survival analysis suite.