PROGRAM

SESSION I: INTRODUCTION TO SURVIVAL ANALYSIS

1. Introduction
2. 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
3. Survival analysis
   • The survivor and hazard functions
   • Hazard models
     • Parametric models
     • Semiparametric models
     • Nonparametric estimators
   • Analysis time (time at risk)
4. Summary

SESSION II: SETTING AND SUMMARIZING SURVIVAL DATA

1. 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
2. After (st) setting your data
   • Look at stset‘s output
   • Use stdescribe
   • Use stvary
   • Perhaps use stfill
3. Example: Hip fracture data
4. Appendices
   • Dates
   • Other formats
   • Convenience options

SESSION III: SETTING AND SUMMARIZING SURVIVAL DATA

1. Nonparametric estimation
2. 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
3. The Nelson–Aalen estimator of the cumulative hazard
4. Alternative estimators of the survivor and cumulative hazard functions
5. 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

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SESSION IV: REGRESSION MODELS — COX PROPORTIONAL HAZARDS

1. 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
2. The calculation of results
   • No tied failures
   • Tied failures
     • The marginal calculation
     • The partial calculation
     • The Breslow approximation
     • The Efron approximation
     • Summary
3. Stratified analysis
   • Obtaining coefficient estimates
   • Obtaining the baseline functions
4. 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

SESSION V: REGRESSION MODELS — PARAMETRIC SURVIVAL MODELS

1. Introduction
2. Classes of parametric models
   • Parametric proportional-hazards models
   • Accelerated failure-time models
3. Maximum likelihood estimation for parametric models
4. 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)
5. Choosing among parametric models
   • Nested models
   • Nonnested models
6. Stratified models
7. Use of predict after streg
   • Predicting time of failure
   • Predicting the hazard and related functions
   • Calculating residuals
   • Use of stcurve after streg