Overview
Teaching Methods
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Four modules together with introductory, policy context and closing sections
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‘Bite-size’ video presentations from members of the Faculty for each module
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Computer-based exercises using MS Excel
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*NEW* Alternative ‘R’-based exercise track for those already familiar with R
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Video ‘run-through’ of each exercise by members of the Faculty
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Online discussion forums monitored by Faculty and tutors
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One ‘live’ question and answer session for each module with Module leads
How It Works
Course Dates
Objectives
By the end of the course, participants will be able to:
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Consider the role of decision modelling in economic evaluation to guide decision making
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Use the basic building blocks of decision analysis such as joint and conditional probabilities and expected values
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Implement the principles of conceptual modelling as a way of planning a model
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Understand the strengths and weaknesses of the decision tree model and build such a model in Excel
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Understand the strengths and weaknesses of the Markov model and build such a model in Excel
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Build a model for a generic diagnostic test and understand how to assess the value of diagnostic information
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Think critically about the structure of decision models in particular situations and apply these appropriately
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Implement key generic analytic steps in decision analysis such as evidence identification and basic synthesis, sensitivity analysis and reporting results
Prerequisites
Faculty

Course Lead Andrew Briggs

Module Lead Karl Claxton

Module Lead Stephen Palmer

Module Lead Claire Rothery

Course Lead Mark Sculpher

Module Lead Alec Miners

R Tutor Lead Nichola Naylor
