Hands on Model Calibration in R
Description and Objectives:
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.
Become a Member
SMDM members contribute critically to health policy research in the areas of evidence-based medicine, cost effective health care, patient decision making and public health. SMDM helps you to be more than just a face in the crowd. The connections you make through SMDM can help you build a network of long-term contacts to help you throughout your career.
"Attending SMDM Meetings has sharpened my research and presentation skills."continue »
MDM offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health policy development.