NetCourse 631: Introduction to Survival Analysis Using Stata

Introduction to survival analysis

• Introduction
• The problem of survival analysis
  • The need for specific distributions
  • Answering specific kinds of questions
  • Censoring
    • Right-censoring (withdrawal from study)
    • Left-censoring
  • Truncation
    • Left-truncation (delayed entry)
    • Right-truncation
  • Gaps
• Survival analysis
  • The survivor and hazard functions
  • Hazard models
    • Parametric models
    • Semiparametric models
    • Nonparametric estimators
  • Analysis time (time at risk)
• Summary

Setting and summarizing survival data

• The purpose of the stset command
  • The desired format—Introduction to stset
  • (st) Setting your data
  • The syntax of the stset command
    • Specifying analysis time
    • Specifying what constitutes failure
    • Specifying when subjects exit from the analysis
    • Specifying when subjects enter the analysis
    • Specifying the subject ID variable
    • Handling gaps
• After (st) setting your data
  • Look at stset‘s output
  • Use stdescribe
  • Use stvary
  • Perhaps use stfill
• Example: Hip fracture data
• Appendices
  • Dates
  • Other formats
  • Convenience options

Setting and summarizing survival data

• Nonparametric estimation
• The Kaplan–Meier product-limit estimator of the survivor curve
  • Calculation of the Kaplan–Meier survivor curve
  • Censored observations
  • Delayed entry
  • Gaps
  • Properties of the Kaplan–Meier estimator
  • The sts graph command
  • The sts list command
  • The stsum command
• The Nelson–Aalen estimator of the cumulative hazard
• Alternative estimators of the survivor and cumulative hazard functions
• Comparing survival experience
  • The log-rank test
  • The Wilcoxon test
  • The Tarone–Ware test
  • The Peto–Peto–Prentice test
  • The Fleming–Harrington test
  • Test for trend across ordered groups
  • The Cox test

Regression models — Cox proportional hazards

• Introduction
  • The Cox model has no intercept
  • Interpreting coefficients
  • The effect of units on coefficients
  • The baseline hazard and related functions
  • The effect of units on the baseline functions
  • Summary of stcox command
• The calculation of results
  • No tied failures
  • Tied failures
    • The marginal calculation
    • The partial calculation
    • The Breslow approximation
    • The Efron approximation
    • Summary
• Stratified analysis
  • Obtaining coefficient estimates
  • Obtaining the baseline functions
• Modeling
  • Indicator variables
  • Categorical variables
  • Continuous variables
  • Interactions
  • Time-varying variables
    • Using stcox with option tvc()
    • Using stsplit
  • Testing the proportional-hazards assumption
    • Tests based on reestimation
    • Test based on Schoenfeld residuals
    • Graphical methods
  • Residuals
    • Determining functional form
    • Assessing goodness of fit
    • Finding outliers and influential points

Regression models — Parametric survival models

• Introduction
• Classes of parametric models
  • Parametric proportional-hazards models
  • Accelerated failure-time models
• Maximum likelihood estimation for parametric models
• A survey of parametric regression models in Stata
  • Exponential regression
    • Exponential regression in the PH formulation
    • Exponential regression in the AFT formulation
  • Weibull regression
    • Weibull regression in the PH formulation
    • Weibull regression in the AFT formulation
  • Gompertz regression (PH formulation)
  • Lognormal regression (AFT formulation)
  • Loglogistic regression (AFT formulation)
  • Generalized log-gamma regression (AFT formulation)
• Choosing among parametric models
  • Nested models
  • Nonnested models
• Stratified models
• Use of predict after streg
  • Predicting time of failure
  • Predicting the hazard and related functions
  • Calculating residuals
• Use of stcurve after streg