Short Courses

SMDM 2022 will include 8 in-person short courses to be held on Sunday, 23 October 2022. 7 virtual short courses will be offered in November after the in-person meeting. 

To register for a short course please visit the Registration page.

An Introduction to Research Prioritization and Study Design Using Value of Information Analysis Please click here to see the short course program

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Course Director: Jeremy D. Goldhaber-Fiebert, PhD, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, USA

Additional Faculty: 
- Anna Heath, PhD, The Hospital for Sick Children, Toronto, ON, Canada, Canada
- Natalia Kunst, PhD, University of Oslo/Norwegian Directorate of Health, Norway

Course Description and Objectives:

Value of information (VOI) is a key concept in decision analysis that can be used to determine research priorities, inform resource allocation for potential further research and design proposed research studies. This course will introduce the general concepts behind VOI, present several key VOI measures and highlight where they can be most useful in directing future research. It will also demonstrate key graphical presentations of these measures and critically evaluate VOI analyses and their underlying assumptions. The purpose of this course is to introduce VOI measures and their use in decision modelling. The course will introduce these measures, discuss their presentation and assumptions.

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

  • Interpret the Expected Value of Perfect Information (EVPI)
  • Interpret the Expected Value of Perfect Partial Information (EVPPI)
  • Interpret the Expected Value of Sample Information (EVSI)
  • Interpret the Expected Net Benefit of Sampling (ENBS)
  • Discuss key assumptions that impact a VOI analysis
Building on Cost-Effectiveness Analysis to Improve the Economic Evaluation of New Medical Interventions: Theory and Practice Please click here to see the short course program

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Course Director: Louis Garrison, University of Washington; Department of Pharmacy, USA

Additional Faculty: 
- Peter Neumann, Tufts University Center for the Evaluation of Value and Risk in Health, USA
- Charles E. Phelps, University of Rochester  Department of Economics, USA
- Shelby D. Reed, Duke University School of Medicine, USA

Course Description and Objectives:

Although conventional cost-effectiveness analysis (C-CEA) based on the quality-adjusted life-year (QALY) is widely used, health economists and others have made efforts to address some of its limitations both in theory and in practice.  Numerous "value assessment frameworks" (VAFs) emerged in several medical decision-making disciplines as the US healthcare system grapples with rising drug prices and the costs of new medical interventions.  The US Second Panel on Cost-Effectiveness Analysis in Health and Medicine report (2017)  was published and an ISPOR Special Task Force on VAFs was convened to address the limitations of C-CEA.  That task force promulgated a conceptual framework diagram (2018), the now widely disseminated "value flower", depicting "novel" elements of value to be considered along with those of C-CEA.  Along with the 2nd Panel report, this diagram, not meant to be definitive, has sparked research into these novel elements.  

The short course objectives are to:  

  • Inform participants about recent developments in refining and expanding on the foundation of C-CEA for the assessment the economic evaluation of new medical interventions.
  • Acquaint participants with the theory and practice underlying these developments from both a more comprehensive conceptual level and for specific proposed novel elements of value:
    • At an overall conceptual level, cover implications for practice of (i) adopting a more comprehensive view of the "societal perspective", and (ii) incorporating considerations of uncertainty and risk aversion via the "Generalized Risk-Adjusted Cost-Effectiveness" (GRACE) theoretical framework.
    • In terms of other, specific novel elements of value, cover the state of the art for estimating the impact the following elements:  health equity, insurance value, severity of disease, the value of hope, real option value, family spillovers, and scientific knowledge spillovers.   
  • Provide examples of the methods, such as discrete-choice experiments (DCE) and multi-criteria decision analysis (MCDA), used to estimate and synthesize this additional information.
Causal Inference and Causal Diagrams in Medical Decision Making Using Big Real World Observational Data and Pragmatic Trials Please click here to see the short course program

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Course Director: Uwe Siebert, MD, MPH, MSc, ScD, UMIT-TIROL, University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment; ONCOTYROL - Center for Personalized Cancer Medicine; Harvard University, Depts. of Epidemiology, Health Policy & Management, and Radiology, Austria

Additional Faculty: 
- Douglas Faries, PhD, Global Statistical Sciences - Real World Analytics, Eli Lilly and Co, USA
- Felicitas Kuehne, MSc, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT-TIROL, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria, Austria

Course Description and Objectives:

This course provides an introduction to the principles of causation, causal diagrams (directed acyclic graphs, DAGs), and methods for causal inference. We will use the framework of "target trial emulation" and counterfactual "replicates" to apply causal methods to big real-world observational data and pragmatic trials and link this framework with causal decision-analytic modeling. 

The objectives of this course are to: 

(1) define causal interventions and actions, draw and interpret causal diagrams, and distinguish causal from non-causal statistical associations. 
(2) decide which biostatistical/epidemiological methods must be used in different situations to derive causal effect parameters.
(3) understand the conceptual approaches of causal decision modeling 
(4) know how these methods are applied in big real-world data and pragmatic trials  

Published examples from different fields will be used to demonstrate how to: 

  • Adjust for time-independent confounders (i.e., confounder affects both risk factor and disease), where standard stratification, regression analysis, propensity score methods and matching/balancing approaches yield valid causal effects if all confounders are measured;
  • Adjust for time-dependent confounding (i.e., the confounder simultaneously acts as an intermediate step in the causal chain between risk factor and disease), where standard regression analysis fails and causal g-methods such as g-formula, inverse probability weighting of marginal structural models or g-estimation of structural nested models must be used;
  • Adjust for non-adherence or treatment switching in randomized clinical trials, where both the intention-to-treat and the naïve per protocol analyses can fail to yield the true causal intervention effect;
  • Assess the “fallibility of estimating direct effects” when adjusting for intermediate steps;
  • Apply the concept of “target trial emulation” and counterfactuals using “replicates” to perform a valid per-protocol analysis of sustained treatment regimens with big real-world data and pragmatic trials with post-randomization confounding.
  • Discuss different biases such as time-independent confounding, time dependent confounding, selection bias, and immortal time bias etc.

 

Decision Counseling as a Method to Facilitate Shared Decision Making Please click here to see the short course program

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Course Director: Ronald E. Myers, DSW, PhD, Thomas Jefferson University, Department of Medical Oncology, USA

Additional Faculty: 
- Mie H. Hallman, MPH, Thomas Jefferson University, Department of Medical Oncology, USA
- Kristine M. Pham, MPH, Thomas Jefferson University, Department of Medical Oncology, USA
- Marguerite L. Samson, Thomas Jefferson University, Department of Medical Oncology, USA

Course Description and Objectives:

The course will include a combination of didactic learning and interactive practice sessions using the Decision Counseling Guide (JDCG)©, an online interactive patient decision support intervention. Trained counselors can use the JDCG to engage patients in shared decision making (SDM) about important health care decisions.

The course will prepare participants to:  

  • Educate patients about decision alternatives (e.g., Option 1 versus Option 2)
  • Elicit and classify patient reasons that favor one option over another
  • Determine the influence and importance of identified patient reasons
  • Clarify the patient’s personal preference related to available options
  • Identify the patient’s decision and develop an action plan
  • Assess the quality of a decision counseling session
Modeling Approaches for Analyzing Health Care Problems – an Introductory Overview and Comparison Please click here to see the short course program

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Course Director: Beate Jahn, Dept. of Public Health, Health Services Research and Health Technology Assessment,  UMIT - University for Health Sciences, Medical Informatics and Technology, Austria

Additional Faculty: 
- Prof. Mark S. Roberts, MD, MPP, Health Policy and Management, University of Pittsburgh  Graduate School of Public Health, USA
- Prof. Dr. Uwe Siebert, MPH, MSc, Dept. of Public Health, Health Services Research and Health Technology Assessment UMIT - University for Health Sciences, Medical Informatics and Technology, Austria
- Prof. James Stahl, MD, CM, MPH, Dartmouth-Hitchcock Medical Center, USA

Course Description and Objectives:

The course starts with a short introduction to decision-analytic modeling. Alternative modeling approaches will then be introduced followed by an interactive discussion.

Session-1: This session covers decision trees (DT) and state-transition Markov models (STMM), two widely used methods. STMMs are based on a set of health states and have been applied in decision analyses addressing questions about prevention, diagnosis and treatment of chronic diseases.

Session-2: The application of microsimulation/individual level simulation allows investigators to model individuals and evaluate heterogeneous populations using various approaches including individual-level state transition models or discrete event (DES). 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 modeling autonomous, interacting agents. The fundamental feature of an agent is the capability to make independent decisions, and complex network structures and contact behaviors can be implemented. ABMs have been used to examine  emerging behavior, economic and health-care research questions (e.g. COVID-19 vaccination). We will cover the role of agents as active model components.

Session-5: SD is a powerful modeling 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.

By the end of this course, participants will

  • understand the role of decision-analytic modeling in health care
  • know the key concepts of six different modeling approaches
  • be able to describe advantages and disadvantages of different modeling approaches
  • be able to critically discuss model selection
In-person afternoon courses (14:00 - 17:30 (Pacific Time))
Advanced Computation of Value of Information Measure to Determine Optimal Research Design Please click here to see the short course program

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Course Director: Anna Heath, PhD, The Hospital for Sick Children, Toronto, Canada

Additional Faculty: 
- Jeremy D. Goldhaber-Fiebert, PhD, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, USA
- Natalia Kunst, PhD, University of Oslo/Norwegian Directorate of Health, Norway

Course Description and Objectives:

This course presents computation methods for the Expected Value of Sample Information (EVSI), a decision-theoretic measure of the monetary value of collecting additional information through potential future research. Participants will discuss EVSI and how it can be used to design research studies. The course will then give a demonstration of how to efficiently compute EVSI in practice with accompanying code provided in R. The purpose of this course is to introduce EVSI as a tool for research prioritization and study design. The course will introduce several recent methods for the calculation of EVSI alongside R code to calculate and present these measures.

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

  • Distinguish four recently developed calculation methods for EVSI
  • Decide which EVSI calculation method is suitable for a given health economic decision model
  • Calculate EVSI in R for two different health economic models
  • Present EVSI analyses using standardized, publication-quality graphics
  • Discuss key assumptions for calculating the Expected Net Benefit of Sampling (ENBS)
  • Design efficient future research studies by determining their optimal sample sizes
Designing and Implementing Simulations of Patient Trajectories Using Spreadsheets Please click here to see the short course program

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Course Director: J.  Jaime Caro, Evidera, USA

Additional Faculty: 
- Jorgen Moller, Evidera, Sweden

Course Description and Objectives:

This course explains the concepts of individual level simulation using the DIC approach. Students will be shown step-by-step how to design and implement a DICE model to structure microsimulations and discrete event simulations. The course covers the components, the mechanics of implementing them, what to focus on during the model design, how to specify the simulation in Excel and how to execute the analyses. This will include deterministic and probabilistic sensitivity analyses, scenarios, and other analytic approaches.  The use of profiles to handle patient and context characteristics that may affect trajectories, outcomes and reflect distributional aspects will be covered. The method will be placed in the context of existing taxonomies of modeling techniques to facilitate a comparison with the traditional methods. Implementation will be illustrated by building live examples.

After attending this short course, a student should be able to:

  • Describe what simulation is and how it can be used for modeling the consequences of medical and public health decisions
  • Design, build and run a simulation using the DICE approach for MS Excel
  • Implement patient profiles to address distributional aspects
  • Contrast DICE simulation with more traditional approaches to modeling.
Measuring the Priorities, Preferences, and Tradeoffs of Patients and Other Stakeholders in Medicine with a Diverse Set of Methods Please click here to see the short course program

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Course Director: John Bridges, The Ohio State University College of Medicine, Department of Biomedical Informatics, USA

Additional Faculty: 
- Norah Crossnohere, The Ohio State University College of Medicine, Department of Biomedical Informatics, USA
- Brett Hauber, CHOICE Institute, University of Washington School of Pharmacy, USA

Course Description and Objectives:

Researchers, patient advocates, policy makers, and providers are increasingly seeking information on priorities, tradeoffs, and preferences of patients and other stakeholders in medicine. Despite the publication of several comprehensive reviews demonstrating that there are a wide variety of relevant methods, potential users are often reluctant to use them because they know less about them. Instead, researcher might turn to using a discrete-choice experiments (DCE) when there might be a more appropriate or simpler method available. This course will introduce participants a diverse set of methods to measure priorities, tradeoffs, and preferences. We will provide participants with background on these methods so that they understand how they differ, provide examples of each method, and describe how they can be used to inform real-world decisions.

By the end of this course participants will have an: 

  • Understanding of the wide variety of methods that can be used to study the priorities, preference, and tradeoffs of patients and other stakeholders.
  • Awareness of the conceptual and practical foundations of a wide variety of methods that can inform the choice among alternative methods.
  • Understanding of best practices to develop, apply, an analyze these methods so as to increase their use in decision making.
Virtual courses

Organised By

Kenes Group, Office: Kenes M+