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.;Many of the SAS examples use DATA step, macro, and SQL programming. The Predictive Modeling Using Logistic Regression and Survival Analysis Using the Proportional Hazards Model courses provide relevant background information. Prior attendance in these courses is advantageous but not required.SAS Products Covered

SAS/STAT

Course Outline

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 Modelingbuilding discrete time hazard models grouped expanded data Hazard Modeling with Big Dataoutcome-dependent sampling data truncation piecewise constant hazards (self-study) Predictive Performancepredictive scoring empirical validation Recurrent Eventsintroduction to recurrent events