Welcome

This is the website for a seven-day hands-on workshop: Decision Modeling for Public Health.

The Decision Analysis in R for Technologies in Health (DARTH) workgroup is offering a workshop on decision modeling for public health using R from Novmeber 2 to 10, 2020 exclusively for the CDC Prevention Effectiveness Fellowship Program. The workshop will be offered virtually via Zoom. Co-instructors Eva Enns, Petros Pechlivanoglou, Hawre Jalal, Fernando Alarid-Escudero, Eline Krijkamp and Alan Yang will cover the principles of decision analytic modeling and will guide participants in implementing Markov models, microsimulation models, probabilistic sensitivity analysis in R. The workshop will also cover more advanced topics, such as model calibration and R Shiny interface design.

Zoom link and meeting info for the workshop:

Meeting ID: 931 9123 2063

Passcode: d22at0h0R

Post-workshop feedback survey

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About this workshop

Health and public health resources are limited and policymakers are under increased pressure to make use of these resources as efficiently as possible. Decision analysis is a quantitative decision-making framework used to formalize objectives, quantify tradeoffs, and ultimately support more informed decision making. These techniques have been applied to a wide range of health policy questions, including optimal cancer screening and treatment guidelines; technology reimbursement and coverage decisions; and hospital operations management.

R is an open-source software that provides a flexible environment where advanced statistical analyses can be combined with decision models of varying complexity within the same framework and the results can be presented in publication-ready tabular and graphical forms. The fact that R is freely available also improves model transparency and reproducibility.

The materials presented in this workshop were developed by the Decision Analysis in R for Technologies in Health (DARTH) Workgroup The DARTH workgroup is a multi-institutional, multi-university collaborative effort comprised of researchers who have a passion for transparent and open-source solutions to decision analysis in health. The aim of this collaboration is to expand knowledge and develop educational materials that empower people to construct R-based decision models. Visit our website for more information.

Part I of this workshop covers the foundational models and analyses used in decision analysis, including cohort state-transition models (Markov models), microsimulation models, and probabilistic sensitivity analysis.

Part II of this workshop covers some advanced decision analysis topics, including model calibration and constructing user-friendly interfaces using R Shiny.

Workship objectives

Overall learning objectives for the course include;

  • Explain research scenarios that require decision analysis methods
  • Develop and manage data for decision analysis
  • Analyze and interpret decision trees and Markov models
  • Analyze and interpret microsimulation models
  • Perform probabilistic sensitivity analysis and model calibration
  • Develop competence in R for performing decision analytic techniques
  • Perform model calibration
  • Create a basic R shiny interface

We will cover the following topics:

Monday, November 2: Introduction to R (Asynchronous)

Tuesday, November 3: Introduction to Decision Analysis (Day 1)

Wednesday, November 4: Cohort State-Transition Models (Day 2)

Thursday, November 5: Microsimulation Models (Day 3)

Friday, November 6: Sensitivity Analysis (Day 4)

Monday, November 9: Model Calibration (Day 5)

Tuesday, November 10: R “Shiny Interface” and Wrap-up (Day 6)

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