Survival Data Mining: Predictive Hazard Modeling for Customer History Data

Business Knowledge Series course

This course is not currently scheduled.

Duration: 3.0 days
Course fee:
EPTO units: .
CEUs: 1.8

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

build models for time-dependent outcomes derived from customer event histories
account for competing risks, time-dependent covariates, censoring, and truncation
use techniques to model current status data and to evaluate the predictive performance of the model.

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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Course fee and EPTO units will differ for on-site training.

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