1 The problem of survival analysis (pdf)

• 1.1 Parametric modeling
• 1.2 Semiparametric modeling
• 1.3 Nonparametric analysis
• 1.4 Linking the three approaches

2 Describing the distribution of failure times

• 2.1 The survivor and hazard functions
• 2.2 The quantile function
• 2.3 Interpreting the cumulative hazard and hazard rate
  • 2.3.1 Interpreting the cumulative hazard
  • 2.3.2 Interpreting the hazard rate
• 2.4 Means and medians

3 Hazard models

• 3.1 Parametric models
• 3.2 Semiparametric models
• 3.3 Analysis time (time at risk)

4 Censoring and truncation

• 4.1 Censoring
  • 4.1.1 Right-censoring
  • 4.1.2 Interval-censoring
  • 4.1.3 Left-censoring
• 4.2 Truncation
  • 4.2.1 Left-truncation (delayed entry)
  • 4.2.2 Interval-truncation (gaps)
  • 4.2.3 Right-truncation

5 Recording survival data

• 5.1 The desired format
• 5.2 Other formats
• 5.3 Example: Wide-form snapshot data

6 Using stset

• 6.1 A short lesson on dates
• 6.2 Purposes of the stset command
• 6.3 Syntax of the stset command
  • 6.3.1 Specifying analysis time
  • 6.3.2 Variables defined by stset
  • 6.3.3 Specifying what constitutes failure
  • 6.3.4 Specifying when subjects exit from the analysis
  • 6.3.5 Specifying when subjects enter the analysis
  • 6.3.6 Specifying the subject-ID variable
  • 6.3.7 Specifying the begin-of-span variable
  • 6.3.8 Convenience options

7 After stset

• 7.1 Look at stset’s output
• 7.2 List some of your data
• 7.3 Use stdescribe
• 7.4 Use stvary
• 7.5 Perhaps use stfill
• 7.6 Example: Hip fracture data

8 Nonparametric analysis

• 8.1 Inadequacies of standard univariate methods
• 8.2 The Kaplan–Meier estimator
  • 8.2.1 Calculation
  • 8.2.2 Censoring
  • 8.2.3 Left-truncation (delayed entry)
  • 8.2.4 Interval-truncation (gaps)
  • 8.2.5 Relationship to the empirical distribution function
  • 8.2.6 Other uses of sts list
  • 8.2.7 Graphing the Kaplan–Meier estimate
• 8.3 The Nelson–Aalen estimator
• 8.4 Estimating the hazard function
• 8.5 Estimating mean and median survival times
• 8.6 Tests of hypothesis
  • 8.6.1 The log-rank test
  • 8.6.2 The Wilcoxon test
  • 8.6.3 Other tests
  • 8.6.4 Stratified tests

9 The Cox proportional hazards model

• 9.1 Using stcox
  • 9.1.1 The Cox model has no intercept
  • 9.1.2 Interpreting coefficients
  • 9.1.3 The effect of units on coefficients
  • 9.1.4 Estimating the baseline cumulative hazard and survivor functions
  • 9.1.5 Estimating the baseline hazard function
  • 9.1.6 The effect of units on the baseline functions
• 9.2 Likelihood calculations
  • 9.2.1 No tied failures
  • 9.2.2 Tied failures
    • The marginal calculation
    • The partial calculation
    • The Breslow approximation
    • The Efron approximation
  • 9.2.3 Summary
• 9.3 Stratified analysis
  • 9.3.1 Obtaining coefficient estimates
  • 9.3.2 Obtaining estimates of baseline functions
• 9.4 Cox models with shared frailty
  • 9.4.1 Parameter estimation
  • 9.4.2 Obtaining estimates of baseline functions
• 9.5 Cox models with survey data
  • 9.5.1 Declaring survey characteristics
  • 9.5.2 Fitting a Cox model with survey data
  • 9.5.3 Some caveats of analyzing survival data from complex survey designs
• 9.6 Cox model with missing data–multiple imputation
  • 9.6.1 Imputing missing values
  • 9.6.2 Multiple-imputation inference

10 Model building using stcox

• 10.1 Indicator variables
• 10.2 Categorical variables
• 10.3 Continuous variables
  • 10.3.1 Fractional polynomials
• 10.4 Interactions
• 10.5 Time-varying variables
  • 10.5.1 Using stcox, tvc() texp()
  • 10.5.2 Using stsplit
• 10.6 Modeling group effects: fixed-effects, random-effects, stratification, and clustering

11 The Cox model: Diagnostics

• 11.1 Testing the proportional-hazards assumption
  • 11.1.1 Tests based on reestimation
  • 11.1.2 Test based on Schoenfeld residuals
  • 11.1.3 Graphical methods
• 11.2 Residuals and diagnostic measures
             aaaaReye’s syndrome data
  • 11.2.1 Determining functional form
  • 11.2.2 Goodness of fit
  • 11.2.3 Outliers and influential points

12 Parametric models

• 12.1 Motivation
• 12.2 Classes of parametric models
  • 12.2.1 Parametric proportional hazards models
  • 12.2.2 Accelerated failure-time models
  • 12.2.3 Comparing the two parameterizations

13 A survey of parametric regression models in Stata

• 13.1 The exponential model
  • 13.1.1 Exponential regression in the PH metric
  • 13.1.2 Exponential regression in the AFT metric
• 13.2 Weibull regression
  • 13.2.1 Weibull regression in the PH metric
    aaaaaaFitting null models
  • 13.2.2 Weibull regression in the AFT metric
• 13.3 Gompertz regression (PH metric)
• 13.4 Lognormal regression (AFT metric)
• 13.5 Loglogistic regression (AFT metric)
• 13.6 Generalized gamma regression (AFT metric)
• 13.7 Choosing among parametric models
  • 13.7.1 Nested models
  • 13.7.2 Nonnested models

14 Postestimation commands for parametric models

• 14.1 Use of predict after streg
  • 14.1.1 Predicting the time of failure
  • 14.1.2 Predicting the hazard and related functions
  • 14.1.3 Calculating residuals
• 14.2 Using stcurve

15 Generalizing the parametric regression model

• 15.1 Using the ancillary() option
• 15.2 Stratified models
• 15.3 Frailty models
  • 15.3.1 Unshared frailty models
  • 15.3.2 Example: Kidney data
  • 15.3.3 Testing for heterogeneity
  • 15.3.4 Shared frailty models

16 Power and sample-size determination for survival analysis

• 16.1 Estimating sample size
  • 16.1.1 Multiple-myeloma data
  • 16.1.2 Comparing two survivor functions nonparametrically
  • 16.1.3 Comparing two exponential survivor functions
  • 16.1.4 Cox regression models
• 16.2 Accounting for withdrawal and accrual of subjects
  • 16.2.1 The effect of withdrawal or loss to follow-up
  • 16.2.2 The effect of accrual
  • 16.2.3 Examples
• 16.3 Estimating power and effect size
• 16.4 Tabulating or graphing results

17 Competing risks

• 17.1 Cause-specific hazards
• 17.2 Cumulative incidence functions
• 17.3 Nonparametric analysis
  • 17.3.1 Breast cancer data
  • 17.3.2 Cause-specific hazards
  • 17.3.3 Cumulative incidence functions
• 17.4 Semiparametric analysis
  • 17.4.1 Cause-specific hazards
    aaaaaaSimultaneous regressions for cause-specific hazards
  • 17.4.2 Cumulative incidence functions
    aaaaaaUsing stcrreg
    aaaaaaUsing stcox
• 17.5 Parametric analysis

References
Author index (pdf)
Subject index (pdf)