Session Details

AM13: Hands-on Model Calibration in R

(Event: SMDM 42nd Annual Meeting: Virtual Meeting)

Oct 20, 2020 9:00AM - Oct 20, 2020 12:00PM

Session Type: Short Course- AM 1/2 Day

Description

Background

In developing mathematical models of disease processes for medical decision making, there are often a subset of model parameters that cannot be observed for physical, practical, or ethical reasons. For example, cancer progression rates prior to detection cannot, by definition, be directly observed. Calibration is the process by which values of uncertain or unknown parameters are estimated such that model outputs match observed clinical or epidemiological data (“calibration targets”). Generally, calibration involves two main components: 1) a strategy for searching through the space of the unknown parameters; and 2) a goodness-of-fit measure that reflects how well a set of model outputs matches the target data. In this course, we will cover how to implement different approaches to each of these steps in R. We will also provide guidance on the pros and cons of different approaches and the circumstances under which some approaches may be more appropriate than others.

Course Type

Half Day

Course Level

Intermediate

Format Requirements

This course will include a brief conceptual presentation of model calibration and an extensive hands-on programming exercise in R using code templates. Participants are assumed to be proficient in R and will need to bring their own laptops with the latest versions of R and RStudio installed. Installation instructions will be provided in advance. This course is intended for individuals familiar with mathematical models (e.g., Markov models, infectious disease models, and/or microsimulation) and their application. Some familiarity with the concepts of model calibration is recommended, but not required.

Description & Objective

In this course, we will cover the steps and decisions involved in calibrating a mathematical model in R. We will begin the course with an overview of when model calibration is necessary and will introduce a general model calibration framework. We will then engage students in an extensive hands-on exercise where they will implement the calibration of a simple mathematical model in R using a simple random search. We will then introduce more advanced calibration approaches, including Latin hypercube sampling, directed search algorithms (e.g., Nelder-Mead), Bayesian calibration, and other iterative calibration approaches (e.g., genetic algorithms). We will discuss the tradeoffs of different calibration approaches and will identify scenarios when one approach may be more appropriate than others.

At the end of the course, participants will be able to:

Understand the choices that must be made when calibrating a model
Correctly interpret the results of a model calibration and visualize these results in R
Implement the calibration of an existing model in R using any of the following approaches:
- Random search
- Latin hypercube sampling
- Directed search with the Nelder-Mead algorithm
- Bayesian calibration using the Metropolis-Hastings algorithm
- Genetic algorithms
Understand the strengths and weaknesses of different calibration approaches

All R programming templates used in this short course will be provided to participants after the course for future use.

Course Director

Eva A. Enns, MS, PhD Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN

Course Faculty

Fernando Alarid-Escudero, PhD Health Policy and Management, Division of Public Administration, Center for Research and Teaching in Economics (CIDE), Aguascalientes, AG, Mexico

Session Fees

	Fee Type	Member Fee	Non-Member Fee
This session is free
	Early:	$40.00	$40.00
	Regular:	$40.00	$40.00
	Late:	$40.00	$40.00
This session is free
	Early:	$20.00	$20.00
	Regular:	$20.00	$20.00
	Late:	$20.00	$20.00