Course Schedule: 21 May 2023, 09:00-12:30

Course Director: Beate Jahn, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria

Additional Faculty: Uwe Siebert, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria

Course Level: Basic

Course Prerequisites:  None. Please bring a simple pocket calculator.

Duration: Half Day

Course Description and Objectives:

Medical decision making is an essential part of health care. It involves choosing an action after weighing the risks and benefits of the options available to the individual patient or the patient population. While all decisions in health care are made under conditions of uncertainty, the degree of uncertainty depends on the availability, validity, and generalizability of clinical data. Medical decision analysis (or decision-analytic modeling) is a systematic approach to decision making under uncertainty that is used widely in medical decision making, clinical guideline development, and health technology assessment of preventive, diagnostic or therapeutic procedures. These analyses can support equity discussions of distribution of health, and it may lead to further ethical discussions. It involves combining evidence for different outcomes and from different sources. Outcome parameters may include disease progression, treatment efficacy/effectiveness, safety, quality of life, and individual patient preferences. Sources may include epidemiological studies, randomized clinical trials, observational studies etc.

The short course objectives are:

• To understand the key concepts and goals of medical decision analysis, 
• To know the basic methods of decision tree analysis and Markov modeling and be able to choose the appropriate model type for a given research question.
• To understand why and when decision-analytic modeling should be used in clinical evaluation
• To be able to critically judge the conclusions derived from a decision-analytic model and know the strengths and limitations of modeling.

Course Schedule: 21 May 2023, 09:00-12:30

Course Director: Marieke de Vries, Radboud University, Institute for Computing and Information Sciences, The Netherlands 

Additional Faculty: N/A

Course Level: Basic

Course Prerequisites: None

Duration: Half Day

Course Description and Objectives:

This course will provide participants with the fundamental components of shared decision making. Specifically, participants will learn the basics about shared decision making including why it is important, how it differs from other related clinical tools (e.g., motivational interviewing, evidence based medicine) and what shared decision making has been proven to do (and not do). We will also discuss how shared decision making occurs in practice, particularly how it can be improved in patient-physician discussions and how risk communication methods can improve decision making. We will briefly describe how other interventions, such as decision support interventions, can promote shared decision making. Additionally, we will discuss evaluation measures for evaluating decision quality and decision aids. Next, we will discuss implementation of shared decision making, particularly in terms of challenges from patients and clinicians perspectives. We will end by discussing future directions in research and clinical practice of shared decision making.

 The short course objectives are:

• Understand the key concepts in shared decision making and patient decision aids
• Being able to distinguish shared decision making from related clinical tools
• Understand the state of the art in the current practice of shared decision making
• Familiarize yourself with examples of intervention to support shared decision making in clinical practice, such as patient decision aids
• Learning how to evaluate decision support interventions using standard internationally recognized criteria to evaluate those interventions
• Understand the main challenges of implementing shared decision making in practice from the perspectives of various stakeholders, including patients and clinicians
• Being able to contribute to discussing future directions in research and clinical practice of shared decision making.

Course Schedule: 21 May 2023, 14:00-17:30

Course Director: M. Elske van den Akker-van Marle, Leiden University Medical Center, Department of Biomedical Data Sciences, Section Medical Decision Making, Netherlands

Additional Faculty: H. (Erik) Koffijberg, University of Twente, Department of Behavioural, Management and Social Sciences, Section Health Technology & Services Research, Health Services Research and Health Technology Assessment, Netherlands

Course Level: Basic

Course Prerequisites: This is an introductory level course; it is for individuals interested in developing their understanding of cost-effectiveness analysis in general, and to enhance their understanding of SMDM posters and presentations.

Duration: Half Day

Course Description and Objectives:

Health economics applies economic theory and science to understand and inform healthcare decisions at an organizational level, recognizing (a) scarcity in healthcare resources and increasing demand, (b) healthcare involves financial choices, and (c) choices need to be value-for-money and fair. However, cost-effectiveness analysis may be viewed as less relevant by patients and physicians delivering care; an inevitable consequence of the tension between maximising society's care over an individual's health outcome.

The short course objectives are:

• To be able to explain more confidently what authors and conference presenters of cost-effectiveness analysis have done, what they have found and what it means
  
• Confidence to engage more thoroughly with the producers of economic evaluations meant to inform medical decision making.

Course Schedule: 21 May 2023, 14:00-17:30

Course Director: Olga Kostopoulou, Imperial College London, UK

Additional Faculty:
Martine Nurek, Imperial College London, UK

Course Level: Basic

Course Prerequisites:

There is no prerequisite knowledge or reading, however, students may wish to read the following before coming to class:

• Kostopoulou, O., Sirota, M., Round, T., Samaranayaka, S., & Delaney, B. C. (2017). The role of physicians’ first impressions in the diagnosis of possible cancers without alarm symptoms. Med Decis Making, 37(1), 9–16. https://doi.org/10.1177/0272989X16644563

Example of a mixed-methods study that investigated the role of first impressions in medical diagnosis.

• Kourtidis, P., Nurek, M., Delaney, B., & Kostopoulou, O. (2022). Influences of early diagnostic suggestions on clinical reasoning. Cognitive Research: Principles and Implications, 7(1), 103. https://doi.org/10.1186/s41235-022-00453-y

Example of a recent experimental study that attempted to attenuate the influence of first impressions using an external aid. 

• Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping Doctors and Patients Make Sense of Health Statistics. Psychological Science in the Public Interest, 8(2), 53–96. https://doi.org/10.1111/j.1539-6053.2008.00033.x

Introduction and review of the magnitude of the problem of understanding and communicating medical risk and statistics.

Duration: Half Day

Course Description and Objectives:

Decision making is important in every field of human activity, and especially in healthcare, where decisions can have a direct impact on patients’ health and survival. In this short course, we discuss decision making as a psychological process, using theories and methods from psychology. We will cover aspects of the decision making of both doctors and patients, since both can determine health outcomes.

By attending this short course, you will understand the psychological processes involved in making clinical judgements and decisions, such as assessments of risk and diagnoses. You will understand how cognitive biases can interfere with practising in an evidence-based way, and how decision making can be improved via debiasing techniques and supported by external aids. We will also discuss the commonest pitfalls in risk communication and the best practices for communicating risk to patients.

By the end of this course, you will be able to:

• Critically evaluate the role of cognition in decision making
• Identify and critically engage with the challenges encountered in risk communication and appraise different formats for communicating risk more effectively.
• Critically reflect upon the factors that influence decision making in your own practice and identify areas for improvement.
• Have in your disposal a repertoire of methods for studying medical decision making as a psychological process.

Course Schedule: 21 May 2023, 09:00-12:30

Course Director: Jeremy D. Goldhaber-Fiebert, Stanford University School of Medicine, Stanford, Department of Health Policy, USA

Additional Faculty:
Natalia Kunst, University of Oslo, Department of Health Management and Health Economics, Norway
Anna Heath, The Hospital for Sick Children & University of Toronto, Child Health Evaluative Sciences, Canada
David Glynn, University of York, Centre for Health Economics, UK

Course Level: Basic

Course Prerequisites: Participants should have some knowledge of health economic evaluation/decision-analytic modeling and Probabilistic Sensitivity Analysis (PSA)

Duration: Half Day

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
• Explore the results of a VOI analysis using graphical displays
• Critically evaluate the assumptions underpinning VOI analyses
• Understand how VOI analysis can be used to determine research priorities and design clinical research.

Course Schedule: 21 May 2023, 09:00-12:30

Course Director: Kristen Berg, CWRU and MetroHealth, Department of Medicine, USA

Additional Faculty:
Adam Perzynski, CWRU and MetroHealth, Department of Medicine, USA
Douglas Gunzler, CWRU and MetroHealth, Department of Medicine, USA

Course Level: Intermediate

Course Prerequisites: A general understanding of regression analysis, multivariate statistics and experimental design is recommended for participants taking this course. The intended audience includes researchers and practitioners interested in understanding latent (unobserved) variables and using cutting edge analytical approaches to test hypothesized relationships between predictors and outcomes. 

Duration: Half Day

Course Description and Objectives:

This short course will make Structural Equation Modeling (SEM) accessible to a wide audience of researchers across many disciplines. SEM is a very general and powerful multivariate technique to link conceptual models, path diagrams, factor analysis and other mathematical models. We present an overview of basic SEM principles, common nomenclature, diagrams, a tiny bit of algebra and how to conduct SEM analyses with many relevant illustrative examples for medical decision makers.

In this course, you will:

• Enrich your way of thinking about medical decision making problems
• Learn fundamental concepts underpinning structural equation models
• Understand advantages of SEM over traditional statistical modeling
• Gain knowledge of resources and techniques for causal modeling
• Be able to interpret results of advanced causal modeling techniques
• Be able to use the results to guide future theorization of mediation process
• Be introduced to software for implementing SEM analyses.    

Course Schedule: 21 May 2023, 09:00-12:30

Course Director: Berhard Holzner, Department of Psychiatry II, Health Outcomes Research Unit, Austria

Additional Faculty: N/A

Course Level: Beginner

Course Prerequisites: None

Duration: Half Day

Course Description and Objectives:

This course on the assessment and use of Patient-reported Outcome (PRO) data for shared-decision making in medicine will capture the following main aspects and related questions: 

• What includes the construct of quality of life / patient reported outcomes?
• How can we assess PROs in clinical trials and in daily clinical routine?
• What are the benefits and barriers of electronic PRO assessment?
• What are the benefits of routine assessment of PROs?
• How can PRO data add to SMD?

The interactive course will have a special focus on oncology but discussions will go beyond.

Course Schedule: 21 May 2023, 09:00-12:30

 Course Director: Joseph Pliskin, Ben Gurion University of the Negev; Department of Health Policy & Management, Israel

Additional Faculty: N/A

Course Level: Basic

Course Prerequisites: None

Duration: Half Day

Course Description and Objectives:

The short course will focus on simple and very powerful tools to improve organizations, based on the workshop leader's extensive practical and academic experience. The methodology presented in the workshop is a synthesis of tools including the Theory-of-Constraints, the Lean methodology and other methods developed and applied in complex systems worldwide.

The short course objectives are:

• Value creation and value drivers in healthcare management.
• The Satisficer approach.
• Bottleneck identification and Constraint Management in complex healthcare systems.
• Increasing throughput: the focusing steps of the Theory of Constraints (TOC)
• Focusing tools: the focusing table and the focusing matrix.
• Reducing response time in complex health service organizations: integrating Lean with TOC.
• Performance measures in healthcare systems: keep it simple…
• The complete kit concept in healthcare systems.

Course Schedule: 21 May 2023, 09:00-17:30

Course Director: John Graves, Vanderbilt University School of Medicine, Department of Health Policy, USA

Additional Faculty:
Shawn Garbett, Vanderbilt University Medical Center, Department of Biostatistics, USA

Course Level: Advanced

Course Prerequisites: Participants should have some experience designing and executing discrete-time Markov models, including experience (a) converting rates to probabilities; (b) constructing a Markov trace from an underlying transition probability matrix; (c) structuring and executing a probabilistic sensitivity analysis (PSA). As the course content will be taught almost exclusively in R, prior experience with Markov models, microsimulation, or discrete event simulation in R is strongly encouraged.

Duration: Full Day

Course Description and Objectives:

This workshop will reinforce and expand advanced techniques not frequently taught in decision modeling courses. The course content centers around an applied exercise whereby a modeler seeks to back-convert an existing discrete-time Markov model, add additional health states and parameters (with uncertainty distributions), and adapt background mortality based on modeled life-table data from a different country, population, or setting. We will demonstrate how the augmented discrete time Markov model can be structured to accurately capture competing events and event dynamics that reflect the underlying continuous-time disease process. The course content will utilize the R programming language, though we will also provide Excel templates for some exercises.

This workshop will focus on important-but-infrequently-taught advanced skills and methods to facilitate the timely and efficient construction, execution, and adaptation of Markov models for health technology assessment and health policy decision-making. We will focus on discrete-time Markov models, though much of the course content applies to other modeling types as well. All course content will be taught in the R statistical programming language, though we will provide additional optional material for how the methods could be adapted to an Excel-based model.

The workshop will center around an applied exercise with the following objectives:

• Back-convert an existing discrete time Markov model to accommodate the inclusion of additional health states and literature-based parameters.
• Adapt background mortality based on modeled life-table data from a different country, population, or setting.
• Inclusion of event accumulators in the embedded transition probability matrix to accurately capture competing events and event dynamics that reflect the underlying continuous-time process.
• Match probabilistic sensitivity analysis (PSA) uncertainty distribution parameters for the augmented model (e.g., parameters governing a normal, lognormal, beta, gamma, etc. distribution) to match values and quantiles ascertained through expert opinion surveys, prior research, etc.

To execute the above objectives, the workshop will provide didactic content, case-studies, and web-based (Shiny) tools for the following topics and methods:

1. Back-converting a discrete-time Markov model into an underlying generator rate matrix using eigenvalue decomposition.
2. Augmenting an underlying rate matrix with new parameters and health states.
3. Including accumulators within an embedded transition probability matrix to accurately account for “jumpover” states that occur when a continuous time process is captured in a discrete-time model.
4. Fitting and characterizing background mortality using a Gompertz model fit to life-table data.
5. Algorithms to solve for uncertainty distribution parameters that match values and quantiles ascertained through expert opinions, prior research, etc.
6. Capturing correlation among uncertain model parameters using PSA sampling via copulas.

Course Schedule: 21 May 2023, 14:00-17:30

Course Director: Anna Heath, The Hospital for Sick Children & University of Toronto, Child Health Evaluative Sciences, Canada

Additional Faculty:
Jeremy D. Goldhaber-Fiebert, Stanford University School of Medicine, Stanford, Department of Health Policy, USA
Natalia Kunst, University of Oslo, Department of Health Management and Health Economics, Norway
David Glynn, University of York, Centre for Health Economics, UK

Course Level: Advanced

Course Prerequisites: Participants require experience using R and knowledge of probabilistic health economic modelling and prior exposure to the Value of Information analysis. Some knowledge of Bayesian statistical methods is helpful but not required.

Duration: Half Day

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   

Course Schedule: 21 May 2023, 14:00-17:30

Course Director: Uwe Siebert, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria

Additional Faculty:
Felicitas Kuehne, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria
Lara Hallsson, UMIT TIROL – University for Health Sciences and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Austria

Course Level: Intermediate

Course Prerequisites: Basic knowledge in epidemiologic methods (confounding)

Duration: Half Day

Course Description and Objectives:

This course will provide an introduction to the principles of causation, causal diagrams (with focus on Directed Acyclic Graphs (DAG)), and methods for causal inference including multivariate analysis and g-methods. We will demonstrate the "target trial emulation" concept with "replicates" applying causal methods to big real-world datasets. The objectives of this course are to draw and interpret causal diagrams, decide which biostatistical/epidemiological methods must be used in different situations to derive causal effect parameters, understand causal decision-analytic modeling, and be able to apply these methods in large real-world data. Published examples will be used to demonstrate how to: (1) use “replicates” to avoid self-inflicted time-related biases (e.g., immortal time bias); (2) use g-methods to appropriately control for time-varying confounding; and (3) how to adjust for compliance in randomized clinical trials, where both "intention to treat" and "naïve per protocol" analyses can fail to yield the true causal intervention effect.

The short course objectives are:

• To develop a target trial protocol and identify the potential of self-inflicted (i.e., avoidable) biases 
• To adjust for non-adherence 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; 
• To assess the “fallibility of estimating direct effects” (i.e., adjusting for intermediate steps);   
• 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, and 
• To 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 methods" such as g-formula, inverse probability weighting of marginal structural models or g-estimation of structural nested models must be used. 
• To derive a causal graph from the data. 
• To support participants in explaining and teaching causal concepts to colleagues within their fields.

Course Schedule: 21 May 2023, 14:00-17:30

Course Director: Kathleen Kieran, University of Washington; Department of Urology, USA

Additional Faculty: Matthew L. Russell, Massachusetts General Hospital, USA

Course Level: Basic

Course Prerequisites: None

Duration: Half Day

Course Description and Objectives:

Divergent objectives and narratives between healthcare team members may lead to suffering and hamper alignment between patient care and the patient’s priorities and wishes. Substituted judgment, in which another person (“proxy”) is making health care decisions for the patient, can further complicate communication. Substituted judgment is common in pediatric and geriatric care, as well is in patients with cognitive conditions. Previous research on shared decision making in pediatric and geriatric patients has found that caregiver involvement, regulations and policies, and communication skills training for clinicians are several key factors perceived as barriers to effective shared decision making. There is a clear need for education about effective communication with the patient and proxy, as well as acquisition of skills to ensure that all perspectives are identified, heard, and understood. In this course, participants will learn and practice the skills necessary to advocate for and partner with patients and their proxies.

The short course objectives are:

• To describe three ways in which substituted judgment can alter the content and delivery of information between a patient/proxy and the health care team.
• To utilize relationship-centered communication skills to elicit stakeholder preferences and priorities and distinguish between the preferences /priorities of patient and proxy.
• To utilize mind-mapping as one example of a technique to gather and organize the perspectives of all stakeholders.