An Introduction to Survival Analysis Using Stata, Revised Third Edition

The problem of survival analysis

Parametric modeling　
Semiparametric modeling
Nonparametric analysis　
Linking the three approaches

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

Truncation

The desired format　
Other formats
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　
Use stdescribe
Use stvary　
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

Using stcox

Likelihood calculations

Stratified analysis

Cox models with shared frailty

Cox models with survey data

Cox model with missing data—multiple imputation

Indicator variables
Categorical variables
Continuous variables

Interactions
Time-varying variables

Modeling group effects: fixed-effects, random-effects, stratification, and clustering

Testing the proportional-hazards assumption

Residuals and diagnostic measures Reye’s syndrome data

Motivation
Classes of parametric models

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

Use of predict after streg

Using stcurve
Predictive margins and marginal effects

Frailty models

Estimating sample size

Accounting for withdrawal and accrual of subjects　

Estimating power and effect size　
Tabulating or graphing results

Cause-specific hazards
Cumulative incidence functions
Nonparametric analysis

Semiparametric analysis

Parametric analysis

Biography

Mario Cleves is Professor and the Biostatistics Section Chief in the Department of Pediatrics at the University of Arkansas for Medical Sciences.

William Gould is the president and head of development 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.

"This is an application-oriented introduction to survival analysis using Stata. The authors have focused on intuitions without getting into technical details. For example … the rather mysterious partial likelihood was elegantly illustrated with a small dataset and simple derivations for conditional probabilities. The book provides an excellent coverage of commonly used nonparametric, semiparametric, and parametric analyses of survival data, with ample application examples. The implementation of each survival approach has been carefully laid out in Stata syntax and real data analyses. Moreover, the material covered in the book is surprisingly comprehensive, including Coxmodels with time-varying covariates, shared frailty models, multiple imputations, and competing risk regression. Those topics are often encountered in practice but usually missing from an introductory book of survival analysis. The revised third edition has been updated to reflect the welcome additions in Stata 14 relative to previous versions. … The revised third edition provides not only an excellent tutorial to anyone who is interested in learning survival models with examples, but also an extremely handy reference to researchers who would like to perform survival analyses in Stata."
—Yu Cheng, University of Pittsburgh, in The American Statistician, April 2018