Survival Data Mining: A Programming Approach
BMCE : BMCE42
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.
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.
Learn How To
- 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 Should Attend
Predictive modelers, data analysts, statisticians, econometricians, model validators, and data scientists
Prerequisites
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.
SAS Products Covered
SAS/STAT
Course Outline
Survival Data Mining
- introduction to survival data mining
- elements of survival analysis
- time-dependent covariates
- semi-parametric survival models
- parametric survival models
- discrete-time survival models
- building discrete time hazard models
- grouped expanded data
- outcome-dependent sampling
- data truncation
- piecewise constant hazards (self-study)
- predictive scoring
- empirical validation
- introduction to recurrent events
Live Class Schedule
Duration: 14 hours
Step into our live classes and experience a dynamic learning environment where you can ask questions, share ideas, and connect with your instructor and classmates. With on-demand lab hours, you can explore the material at your own pace. Our globally acclaimed instructors will motivate you to think bigger, so you can take what you've learned and achieve your biggest goals.
This course isn't publicly scheduled, but private training and mentoring may be available. Contact us to explore options.
Private Training
Get training tailored specifically for your team, led by expert SAS instructors. Choose from virtual sessions, or training at your location (or ours). Perfect for teams seeking a customized curriculum and plenty of interaction with a SAS specialist. We'll schedule it at a time that works for you.
Mentoring Services
Take your training to the next level with personalized mentoring. While private training offers structured coursework, mentoring provides hands-on, real-time support from a subject matter expert. As you work with your own data, you'll receive expert guidance to help you uncover insights, unlock the full potential of your data, and make faster progress. Perfect for those looking to apply what they’ve learned and see quicker results.