Survival Data Mining: A Programming Approach

Course Details
Code: BMCE42
Tuition (USD): $1,650.00 • Classroom (2 days)
$1,650.00 • Virtual (2 days)

This advanced course discusses 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.

Skills Gained

  • build models for time-dependent outcomes derived from customer event histories
  • account for competing risks, time-dependent covariates, right censoring, and left truncation
  • handle large data sets
  • compute the expected value of the remaining time until an event
  • evaluate the predictive performance of the model.

Who Can Benefit

  • Predictive modelers, data analysts, statisticians, econometricians, model validators, and data scientists


  • Before attending this course, you should
  • have a basic understanding of survival analysis
  • have experience with predictive modeling, particularly with logistic regression
  • be familiar with statistical concepts such as random variables, probability distributions, and parameter estimation
  • be familiar with SQL (including topics such as sub-queries and left-joining)
  • have SAS programming proficiency.

Course Details

Survival Data Mining

  • introduction to survival data mining
  • elements of survival analysis
  • time-dependent covariates

Survival Models (Self-Study)

  • semi-parametric survival models
  • parametric survival models
  • discrete-time survival models

Flexible Hazard Modeling

  • building discrete time hazard models
  • grouped expanded data

Hazard Modeling with Big Data

  • outcome-dependent sampling
  • data truncation
  • piecewise constant hazards (self-study)

Predictive Performance

  • predictive scoring
  • empirical validation

Recurrent Events

  • introduction to recurrent events
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