An Introduction to Survival Analysis Using Stata, Third Edition

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.

Table of Contents:
The Problem of Survival Analysis Parametric modeling Semiparametric modeling Nonparametric 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 Hazard Models Parametric models Semiparametric models Analysis time (time at risk) Censoring and Truncation Censoring Truncation Recording Survival Data The desired format Other formats Example: Wide-form snapshot data Using stset A short lesson on dates Purposes of the stset command Syntax of the stset command After stset Look at stset’s output List some of your data Use stdescribe Use stvary Perhaps use stfill Example: Hip fracture data Nonparametric Analysis 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 Using stcox Likelihood calculations Stratified analysis Cox models with shared frailty Cox models with survey data Cox model with missing data–multiple imputation Model Building Using stcox Indicator variables Categorical variables Continuous variables Interactions Time-varying variables Modeling group effects: fixed-effects, random-effects, stratification, and clustering The Cox Model: Diagnostics Testing the proportional-hazards assumption Residuals and diagnostic measures Parametric Models Motivation Classes of parametric models A Survey of Parametric Regression Models in Stata The exponential model Weibull regression 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 Using stcurve Generalizing the Parametric Regression Model Using the ancillary() option Stratified models Frailty models 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 Competing Risks Cause-specific hazards Cumulative incidence functions Nonparametric analysis Semiparametric analysis Parametric analysis References Author Index Subject Index

About the Author :
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.

Review :
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