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M2008

Survival Data Mining: Predictive Hazard Modeling for Customer History Data

Business Knowledge Series course

Duration: 3.0 days
Course fee: $2,175
EPTO units: 4.2
CEUs: 1.8
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Presented by Bob Lucas, Ph.D., Director of Statistical Training and Technical Services, SAS, based on materials created by Will Potts

This advanced course identifies the benefits and pitfalls of using survival analysis for buisness intelligence. Designed for data analysts, it covers both theoretical justification of various survival data mining methods and their practical implementation using SAS software.

Learn how to

Who should attend

Predictive modelers, data analysts, and statisticians

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Prerequisites
Before attending this course, you should
  • have experience with predictive modeling, particularly with logistic regression
  • be familiar with statistical concepts such as random variables, probability distributions, and parameter estimation
  • be comfortable working with summation notation, vectors, matrices, and analytic geometry
  • have SAS programming proficiency.
Many of the SAS examples use DATA step, macro, and SQL programming. Modeling methods are implemented using SAS/STAT procedures and SAS Enterprise Miner. The Predictive Modeling Using Logistic Regression and Neural Network Modeling courses provide relevant background information. Prior attendance in these courses is advantageous but not required.
Course Contents
Survival Data
  • time-dependent outcomes derived from customer event histories
  • features of the event-time distribution such as competing risks, time-dependent covariates, censoring, and truncation
  • basic nonparametric estimation of the hazard and distribution functions
Flexible Parametric Hazard Models
  • multinomial logistic regression for right censored data
  • regression spline and neural network modeling
  • adaptations for large data sets
Modeling Current Status Data
  • simple models for reduced sample data
  • powerful models for cross-sectional data
Predictive Performance
  • predictive scoring
  • estimating the mean residual lifetime
  • empirical validation using concentration curves
Software
This course addresses SAS/STAT, SAS Enterprise Miner.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.
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