Developing the statistical concepts unique to survival data, An Introduction to Survival Analysis Using Stata, Revised Edition includes statistical theory, step-by-step procedures for analyzing survival data, a detailed usage guide for Stata's most widely used st commands, and pointers for using Stata to analyze survival data and present the results. The first several chapters cover basic theoretical concepts as well as censoring and truncation. The next few chapters describe the formatting, manipulation, stsetting, and error-checking involved in preparing survival data for analysis using Stata's st analysis commands. The book also discusses Cox regression and includes various examples of fitting a Cox model, obtaining predictions, interpreting results, building models, and modeling diagnostics. The final chapters cover parametric models, which are fitted using Stata's streg command.

Table of Contents:
Preface to the Revised Edition Preface Notation and Typography 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 hazard and cumulative hazard 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 USING stset A short lesson on dates The purpose of the stset command The syntax of the stset command AFTER stset Look at stset's output List some of your data Use stdes 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 Tests of hypothesis THE COX PROPORTIONAL HAZARDS MODEL Using stcox Likelihood calculations Stratified analysis Cox models with shared frailty MODEL BUILDING USING stcox Indicator variables Categorical variables Continuous variables Interactions Time-varying variables THE COX MODEL: DIAGNOSTICS Testing the proportional hazards assumption Residuals PARAMETRIC MODELS Motivation Classes of parametric models A SURVEY OF PARAMETRIC REGRESSION MODELS IN STATA The exponential model Weibull regression Gompertz regression (PH metric) Log-normal regression (AFT metric) Log-logistic regression (AFT metric) Generalized gamma regression (AFT metric) Choosing among parametric models POST-ESTIMATION COMMANDS FOR PARAMETRIC MODELS Use of predict after streg Using stcurve GENERALIZING THE PARAMETRIC REGRESSION MODEL Using the ancillary() option Stratified models Frailty models References Author Index Subject Index