Survival Data Mining: A Programming Approach

  • Predictive modelers
  • Data analysts
  • Statisticians
  • Econometricians
  • Model validators
  • Data scientists

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Expected Duration
3 day


This advanced course covers predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods and their practical implementation.

Note: Formerly titled “Survival Data Mining: Predictive Hazard Modeling for Customer History Data,” this course now includes hands-on exercises so that you can practice the techniques that you learn. Other additions include a chapter on recurrent events, new features in SAS/STAT software, and an expanded section that compares discrete time approach versus the continuous time models such as Cox Proportional Hazards models and fully parametric models such as Weibull.


1. Survival Data Mining

  • Introduction to survival data mining
  • Elements of survival analysis
  • Time-dependent covariates

2. Survival Models

  • Semi-parametric survival models
  • Parametric survival models
  • Discrete-time survival models

3. Flexible Hazard Modeling

  • Building discrete time hazard models
  • Grouped expanded data

4. Hazard Modeling with Big Data

  • Outcome-dependent sampling
  • Data truncation
  • Piecewise constant hazards

5. Predictive Performance

  • Predictive scoring
  • Empirical validation

6. Recurrent Events

  • Introduction to recurrent events



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