Day 1: Decision Trees and Markov Models
Learning objectives
By the end of Day 1, participants should be able to:
- Understand how to organize R code for decision analytic modeling
- Implement a decision tree in R
- Implement a basic cohort state-transition model in R
- Understand how to calculate epidemiological and economic outcomes in a state-transition model
- Start understanding how to implement your own model in R
Day 1 schedule
| Time | Material |
|---|---|
| 10:00am-10:15am | Workshop orientation / Introduction to model types |
| 10:15am-11:00am | Structuring your R code / Decision trees in R |
| 11:00am-12:00pm | Code walk through: cSTM 3-state model |
| 12:00pm-12:30pm | Lunch |
| 12:30pm-2:00pm | Hands-on exercise: Code your own cSTM with sick-sicker example |
| 2:00pm-3:00pm | Incorporating time dependency in cSTM |
| 3:00pm-4:00pm | Exercise recap |
Materials for Morning Session
Download slides: Introduction to the course
Download slides: Decision Tree Modeling in R: Example
Materials for Afternoon Session
Download demo code: 3-state cSTM (simulation-time dependency)
Download demo code (tunnels): 3-state cSTM (state-residence time dependency)