2020/09/11 - 2020/09/11 | Seoul, KR
This course will be offered in Seoul, KR on September 11th, 2020. The training location will be:
Namsan Square (Kukdong B/D) 7F
173 Toegye-ro, Jung-gu
Seoul, 04554, Korea
The course will run from 8:30 AM to about 5:15 PM.
Click here to register your interest in this course.
This one-day course extends the two-day training into advanced modeling techniques and tools.
Module 1 shows how to make models more realistic when a combination of factors – treatment, health state, patient history – affect the probability of future events. The events then drive long-term changes in probabilities, costs and utilities.
Module 2 introduces Discrete Event Simulation (DES) models, where Markov transition probabilities are replaced by time-to-event distribution sampling. We start with DES models using fixed risks, then extend this technique with more complex time-to-event calculations.
Modules 3 & 4 introduce techniques to validate your models by reviewing patient pathways and internal model calculations.
Modules 5 & 6 introduce the advanced modeling techniques to handle population dynamics and patient interaction.
Module 7 demonstrates how to examine results for a subgroup within overall results for a heterogeneous patient population.
- Complex Markov Simulation Models
- Calculate event probabilities based on a complex combination of treatment, health state and patient history
- Apply the impact of patient history to long-term costs and utilities
- Discrete Event Simulation Models
- Build a Discrete Event Simulation (DES) model, replacing Markov event probabilities with DES time-to-event distribution sampling
- Integrate non-fixed risk time-to-event distributions into the model
- Run simulation and sensitivity analyses on DES models
- Patient Tracking Reporting
- Track patients in model to generate patient tracking reports that follow each patient through the entire model
- Generate cohort-level “Markov-like” reporting from patient-tracking data
- Review Internal Calculation Details
- Output internal trace data to the console to validate model calculations
- Population Dynamics and Budget Impact
- Integrate dynamic cohort size into a Markov model to study population dynamics and budget impact
- Patient Interaction
- Run patients in a synchronized time environment to represent patient interaction and/or resource constraints
- Subgroup Analysis
- Integrate real patient data for simulated patient characteristics through bootstrapping
- Filter simulation results to study subgroups within a heterogeneous cohort
Note that experience with TreeAge Pro is required as a prerequisite for the course. You should be comfortable with all topics covered in the Two-Day Healthcare Modeling course.