Objectives

• To produce guidance on extrapolation of Randomised Clinical Trial (RCT) results using Real World Data (RWD), either observational or registry data, to allow cost-effectiveness analysis (CEA) of new health care technologies to be undertaken

• To develop a new, publicly accessible platform to allow direct modelling of CEA by Health Technology Assessment (HTA) agencies

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Methodology

• Extrapolation of health benefits by using different sets of data

• Assessment and evaluation of existing ways of extrapolation of treatment costs

• Creation of platform based on Discretely Integrated Condition Event (DICE) simulation, to model health benefits and treatment costs during CEA modelling

• Pilot using the DICE model with a published HTA guideline and provision of recommendations for modelling the consequences of guidelines, both in general and when using DICE simulation

 

Outputs

• Deliverable D2.1: A review of parametric model choice used to extrapolate RCT outcomes [PDF]
   

• Deliverable D2.2: Validation of the DICE modelling technique against existing methods and speeding up execution of the simulation engine [PDF]
   

• Deliverable D2.3: Training modules and manuals for HTA staff, reviewers, and modelers (available on the DICE Platform)
   

• DICE platform based on Discretely Integrated Condition Event simulation to model health benefits and treatment costs during CEA modelling. 
   

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Publications about DICE

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• Validation of a DICE simulation against a Discrete Event Simulation implemented entirely in code [https://doi.org/10.1007/s40273-017-0534-0]
   

• Discretely Integrated Condition Event (DICE) simulation for pharmacoeconomics [https://doi.org/10.1007/s40273-016-0394-z]
   

• Leveraging DICE (Discretely-Integrated Condition Event) simulation to simplify the design and implementation of hybrid models [https://doi.org/10.1016/j.jval.2020.03.009]
   

• Adding events to a Markov Model using DICE simulation [www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1177%2F0272989X17715636]
   

• Cooking up a transparent model following a DICE recipe [https://doi.org/10.1007/s40273-019-00840-2]
   

• Economic evaluation of sequences of biological treatments for patients with moderate-to-severe Rheumatoid Arthritis and inadequate response or intolerance to Methotrexate in France [https://doi.org/10.1016/j.jval.2019.12.003]
   

• Trusting the results of model-based economic analyses: Is there a pragmatic validation solution? [https://doi.org/10.1007/s40273-018-0711-9]
   

• Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4 [http://dx.doi.org/10.1016/j.jval.2012.04.013]
   

• Improving transparency in decision models: Current issues and potential solutions [https://doi.org/10.1007/s40273-019-00850-0]
   

• Pharmacoeconomic analyses using discrete event simulation [https://doi.org/10.2165/00019053-200523040-00003]
   

• Advantages and disadvantages of discrete-event simulation for health economic analyses [https://doi.org/10.1586/14737167.2016.1165608]
   

• Discrete event simulation: the preferred technique for health economic evaluations? [https://doi.org/10.1111/j.1524-4733.2010.00775.x]
   

• Caro JJ, Möller J. Chapter 10. DICE Simulation: A Unifying Modeling Approach for Pharmacoeonomics. Pharmacoeconomics From Theory to Practice. Arnold RJ (ed). CRC Press, Oxford, UK and Boca Raton, FL. 2nd edition 2020.
   

• Caro JJ, Möller J, Santhirapala V, Gill H, Johnston J, El-Boghdadly K, Santhirapala R, Kelly P, McGuire A (2021) Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories. Value Health. doi: 10.1016/j.jval.2021.05.023