MODELING APPROACHES FOR ANALYSING HEALTH CARE PROBLEMS – AN INTRODUCTORY OVERVIEW AND COMPARISON
Course Level: Intermediate
Overview: This course provides an overview of various decision-analytic modelling approaches: 1) Decision trees, 2) State Transition Markov Models, 3) Microsimulations, 4) Discrete-Event-Simulations, 5) Agent-based Models and 6) System Dynamics. It provides a unique opportunity to gain insight into alternative modeling techniques and discuss model selection with the several experts in the fields. This course will also consider the new results and best practice recommendations of the ISPOR-SMDM Joint Modeling Good Research Practice Task Force. This course will be useful for those not familiar with simulations and for those who are already experienced in one method, but want to look ‘outside the box’.
Background: Decision-analytic modelling is increasingly applied to analyze decisions under conditions of uncertainty to allocate limited resources in health care. Decisions range from the evaluation of preventions, diagnostic or treatment up to scheduling and planning of health care resources. Decision-analytic models are powerful tools allowing estimation of long-term benefits, risks and harms. Intermediate outcomes of clinical trials can be linked with long term observational studies and cost-effectiveness studies can be done across jurisdictions. The appropriate model type is determined by the research question, nature of the disease, required level of detail, and complexity. Commonly used modelling techniques are: 1) Decision Trees (DT), 2) State Transition Markov Models (STMM), 3) State Transition Microsimulation Models 4) Discrete-Event-Simulation (DES), 5) Agent-based Models (ABM), and 6) System Dynamics (SD).
Format Requirements: This course consists of lectures and interactive discussions. Within five sessions, participants will gain insights into six alternative modelling approaches: 1) DT/STMM, 2) Microsimulation, 3) DES, 4) ABM and 5) SD. The final session provides a unique opportunity to discuss model selection with the several experts. This course will also consider the new results and best practice recommendations of the ISPOR-SMDM Joint Modeling Good Research Practice Task Force. The intended audience includes researchers from all substance matter fields. This course will be useful for those not familiar with simulations and for those who are already experienced in one method, but want to look ‘outside the box’.
Description and Objectives:
1) understand the role of decision-analytic modelling in health care
2) know the key concepts of six different modelling approaches
3) be able to describe advantages and disadvantages of different modelling approaches
5) be able to critically discuss model selection
The course starts with a short introduction to decision-analytic modelling. Alternative modelling approaches will then be introduced in five sections, each followed by an interactive discussion.
Session 1: This session covers DT and STMM (cohort simulation), two widely used methods. STMMs are based on a set of health states (state-transition models) and have been applied in decision analyses addressing questions about prevention, diagnosis and treatment of chronic diseases.
Session 2: The application of microsimulation in decision analysis allows investigators to model individuals and evaluate heterogeneous populations. Approaches range from individual-level state-transition models to DES and equation-based models. This session gives a general introduction based on their applications in the social sciences, health care and politics.
Session 3: DES is a microsimulation method in which entities (e.g., patients) interact and compete for resources (e.g., hospital beds or organ transplants). We will cover the primary components of DES such as entities, attributes, resources, and queues.
Session 4: ABM is a relatively new approach to modelling autonomous, interacting agents. The fundamental feature of an agent is the capability to make independent decisions. ABMs have been used to examine economic issues and questions in the areas of emerging behavior and epidemiology. We will cover the role of agents as active model components.
Session 5: SD is a powerful modelling method that involves both qualitative and quantitative approaches. It takes a "whole system" view, demonstrating how a small change in one part of a system can have major unanticipated effects elsewhere, an aspect that is particularly suitable for healthcare applications.
Beate Jahn, PhD
Department of Public Health, Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology
Department of Public Health and Health Technology Assessment
Mark S. Roberts, MD, MPP
University of Pittsburgh Graduate School of Public Health
Professor and Chair, Professor of Medicine
Health Policy and Management
Uwe Siebert, MD, MPH, MSc, ScD
UMIT, Dept. of Public Health, Health Services Research & HTA / Harvard Univ., Dept. Health Policy & Management, Institute for Technology Assessment / Oncotyrol - Center for Personalized Cancer Medicine
Prof. of Public Health (UMIT), Adjunct Prof. of Health Policy and Management (Harvard Univ.)
UMIT, Dept. of Public Health, Health Services Research and Health Technology Assessment
James Stahl, MD, CM, MPH
Dartmouth-Hitchcock Medical Center
Associate Professor of Medicine