Learning Objectives
Lecture 1: Conceptual and theoretical frameworks
-Construct and solve a decision problem by calculating an intervention’s expected value across competing strategies in a decision tree
-Determine the decision threshold across a range of scenarios -Differentiate between joint and conditional probabilities and demonstrate their use in decision trees
Lecture 2: Foundations of economic evaluation
-Identify theoretical and methodological differences between different economic evaluation techniques -Understand the concepts of summary measures of health, including quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) -Be familiar with the steps of valuing costs in economic evaluations
Lecture 3: Incremental CEA
-Discuss differences between cost-benefit analysis and cost utility analysis -Cover differences between average and incremental cost effectiveness ratios -Characterize decision problems by whether they are competing or non-competing. -Define and discuss incremental cost effectivenness ratios -Discuss concepts of dominance and extended dominance.
Lecture 4: Intro to Markov MOdeling
-Discuss pros and cons of decision modeling using decision trees vs. a formal deterministic model. -Understand the components and structure of discrete time Markov models. -Discuss how to structure and parameterize a transition probabilitiy matrix. -Understand how to construct a Markov trace using a transition probability matrix and state occupancy vector.
Lecture 5: Structuring the Model
-Understand differences between rates and probabilities, hazard rates, relative risks, and other relevant model inputs. -Understand rate-to-probability conversion formulas and transition probability embedding. -Explain how to embed a transition probability matrix with a defined timestep.
Lecture 6: Curating model parameters
- Classify model parameters across different economic perspectives
- Characterize the data collection approaches for cost-effectiveness studies (i.e., alongside clinical trials versus secondary data collection)
- Identify resources and tools for collecting both cost and benefit parameters
Lecture 7: Summarizing model outputs
-Be able to derive summary outcomes (costs and health outcomes) from a Markov trace -Be able to apply methods for: -Cycle correction -Discounting a stream of costs (or benefits) and calculating net present value -Adjusting costs measured in different currencies to a common currency (and currency year)
Lecture 8: Deterministic sensitivity analysis
- Explain the purpose of deterministic sensitivity analysis and provide examples of one-way versus two-way analyses
- Detail the advantages/disadvantages of deterministic sensitivity analysis
Lecture 9: Probabilistic Sensitivity Analysis
- Explain how to draw parameter values from an uncertainty distribution.
- Understand inputs and outputs of a PSA
- Characterize decision uncertainty using cost-effectiveness acceptability curves and frontiers.
Lecture 10: Dissemination
- Discuss how to present and summarize decision analytic models for a non-technical audience.
- Discuss dissemination channels and how best to maximize impact.
Lecture 11: Advanced modeling techniques
- Be able to understand the basic concepts and identify the strengths and limitations of alternative modeling techniques, including microsimulation, discrete event simulation, and infectious disease (dynamic) models