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Mt Hood/SMDM Asia 2023: Workshops  

There will be two half-day workshops held alongside the conference. The workshops take place on Sunday 3rd of December. Introduction to Building Simulation Models in R will take place 10am-1 p.m. and Diabetes Simulation Modelling will be (2-5 p.m.). Lunch (1-2pm) will be provided.

The workshop covers all aspects of building and using health-economic diabetes simulation models. The workshop will have no assumed knowledge and is intended to provide an overview of the field, including the latest developments. 


Introduction to diabetes modelling

  • Brief History

  • How simulation models work

  • Constructing risk equations using individual data

  • Developing risk-factor equations

Quality of life and complications

  • Collection of Quality of life data: Case studies from UKPDS and ADVANCE studies

  • How often and what do we need to collect?

  • Heterogeneity in responses across regions

  • Should be using levels or changes in Quality of life?

  • Relationship between utility and mortality

  • Quality Adjusted Survival Models

  • Role of meta-analysis 

Costs of treatments and complications

  • Changes in the price and expenditure of diabetes therapies:  recent evidence

  • Options for collecting resource use information

  • Sources of costing data in other countries – Sweden, Australia, ADVANCE.

Future directions in modelling

  • Adapting models across settings

  • Calibration risk equations 

  • Developing new equations – mortality following events -  WA UKPDS example

  • LE calculators (Sweden & WA)

  • What can we learn from meta-models?

New Developments in Type 1 diabetes

  • Burden of the disease: Life expectancy gap in Sweden & Australia

  • How a hypo can impact on your life expectancy

  • Overview of a new Type 1 diabetes model

The future of diabetes simulation modelling

  • Capturing new treatments and interventions

  • Can we develop a universal model?

  • Software for simulation modelling

Summary of UKPDS Outcomes Model equations
Overview of HE diabetes simulaton models


Professor Philip Clarke was instrumental in the development of both versions of the UKPDS Outcomes Model.  More recently he has been involved in the development of a comparable Type 1 diabetes simulation model using data from a large diabetes registry in Sweden. He has also been involved with the economic analyses of the major diabetes clinical trials including the UKPDS, FIELD and ADVANCE studies.

The workshop covers the main aspect of constructing and calibrating decision models using R. The workshop assumes some familiarity with concepts of decision-analytic simulation models but is aimed at researchers interested in learning to implement simulation models using R software and fitting them via calibration to data.


Reasons for modeling in R: Availability of excellent, high performance free tools like R and RStudio. Excellent packages for statistical and data manipulation tasks, frameworks like DARTH for decision modeling, and other packages for optimization and calibration. Easy to distribute and reproduce models transparently which is increasingly becoming the standard for submissions to health technology assessment organizations as well as for publication and dissemination.


  • Brief review of the reasons for modeling in R

  • Brief review of basic R functions that are commonly used in decision modelling (import/export data, data handling, basic distributions, “if” and “for” loops etc.) will be provided.

  • A simple decision tree will be constructed using R. A base-case analysis, as well as one-way deterministic and probabilistic analyses will be conducted.

  • A Markov model will be designed using R. A Base case, as well as multi-way sensitivity analysis will be conducted.

  • Results of both models will be presented in tabular and graphical form.

  • Several examples of calibrating the Markov model to data to inform unknown/uncertain inputs will be shown.

  • Advanced functions of R in decision modelling will be discussed. Examples include building microsimulation models or integrating network meta-analyses and decision models using R.

  • Principles of good modelling practices using R (e.g. consistency, proper documentation etc) will be outlined.

  • All R programming templates for decision modelling will be provided to participants after the course for future use along with a list of citations of the papers used in the examples and course.

Introduction to Building and Calibrating Simulation Models in R


Jeremy Goldhaber-Fiebert is a Professor of Health Policy at Stanford University where he is a member of the Department of Health Policy in the School of Medicine and a Core Faculty Member of the Center for Health Policy in the Freeman Spogli Institute.
His research focuses on complex policy decisions surrounding the prevention and management of increasingly common, chronic diseases and the life course impact of exposure to their risk factors

Fernando Alarid-Escudero, Ph.D., is an Assistant Professor of Health Policy, affiliated with the Department of Health Policy in the School of Medicine and the Center for Health Policy at the Freeman Spogli Institute.


His research focuses on developing statistical and decision-analytic models to identify optimal prevention, control, and treatment policies to address a wide range of public health problems and develops novel methods to quantify the value of future research. He has contributed theoretically and methodologically to the fields of decision sciences, disease modeling, cost-effectiveness analysis (CEA), and value of information analysis (VOI).


Dr. Alarid-Escudero is part of the Cancer Intervention and Surveillance Modeling Network (CISNET), a consortium of NCI-sponsored investigators that includes modeling to improve our understanding of the impact of cancer control interventions (e.g., prevention, screening, and treatment) on population trends in incidence and mortality.

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