Mario Cleves, William W. Gould, Roberto G. Gutierrez, and Yulia Marchenko

Table of contents

List of Tables
List of Figures


Preface to the Second Edition
Preface to the Revised Edition
Preface to the First Edition
Notation and Typography


• 1 The problem of survival analysis
  • 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 The 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
      
      
• 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
    • Reye’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
      Fitting 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
    

  References
  Author index
  Subject index