The Graduate Certificate Programme includes four semester courses:
GMS5221: Introduction to Health Econometrics (Feb 2026)
This course is the first of two econometrics courses which aim to equip students with a fundamental understanding of the econometric methods commonly applied in health economics and outcomes research (HEOR. Econometric methods can be used to quantify the direct (medical) and indirect burden of diseases and risk factors, and evaluate the effectiveness of health technologies and policy interventions (key inputs into cost-effectiveness and budget impact analyses). The course will include: a review of probability and statistics, linear regression models and their assumptions, multivariate linear models, and binary outcome models. Alongside learning theory, students will learn and practice the implementation of basic econometric methods using Stata.
Upon completion of the course, students are expected to:
- Understand the basic theory and assumptions underpinning simple linear regression estimation.
- Understand model diagnostics, approaches to model selection, and common sources of bias in regression models.
- Be familiar with Stata, the most commonly used software for health economic analyses, and basic Stata programming.
This course will require Stata.
GMS5222: Introduction to Health Economic Modelling (Feb 2026)
This course aims to equip students with skills required to perform their own cost-effectiveness analyses of health technologies and to critically assess the quality of evaluations conducted by others. It begins with an introduction to key concepts in economic evaluation and decision-analytic modelling. We then transition to building decision-analytic models. We focus on two models commonly used in cost-effectiveness analyses in healthcare: namely decision trees and Markov models. The course includes hands-on-training on how to build these models in Microsoft Excel using real-world examples, so students understand how to implement these methods from first principles.
Upon completion of the course, students are expected be able to:
- Quantify economic burden, cost interventions, and value health gains.
- Conduct cost-effectiveness analysis.
- Implement decision trees and Markov models in Excel.
- Conduct deterministic and probabilistic sensitivity analysis of their models.
- Critically evaluate cost-effectiveness studies
This course will require Microsoft Excel.
GMS5223: Advanced Health Econometrics (18 Aug to 17 Nov 2025)
This advanced course aims to expand students’ econometric knowledge from basic statistics and linear modelling to the methods suitable to handle the complexities of the types of data and questions one has to grapple with in real-world health economics analyses.
This will include (1) how to model count outcomes, such as the number of admissions and lengths of stay, (2) how to model censored and non-normally distributed continuous outcomes, such as medical expenditures, and (3) how to make causal inferences using experimental and observational data. To do so, this course will cover the basic theory and rationale for generalized linear models, count models, two-part models, and the econometrics of study designs that address endogeneity (such as randomized experiments, matching, difference-in-differences, regression discontinuity designs, instrumental variables and two-stage least squares). Alongside lectures covering the theory of these models, students will gain familiarity with how to use them in-practice through class exercises and a course project.
Upon completion of the course, students are expected to:
- Understand the advanced econometric methods required for conducting burden of illness analyses and policy evaluation.
- Know how to appropriately choose model specifications given data and research questions of interest.
- Know how to estimate advanced econometric models using Stata.
This course will require Stata and basic Stata programming knowledge.
GMS5224: Advanced Health Technology Assessment Methods (20 Aug to 19 Nov 2025)
Building on key concepts introduced in the introductory course, this course moves students from modelling in Microsoft Excel, which is helpful to ensure a basic understanding of cost-effectiveness analysis, to modelling in TreeAge Pro. TreeAge Pro is a specialized software that offers a range of built-in functions for more sophisticated models. Using real-world examples, we will introduce students to advanced modelling strategies.
Upon completion of the course, students are expected to be able to:
- Conceptualize and validate decision-analytic models according to best practices.
- Identify and synthesize evidence for economic evaluation.
- Use TreeAge Pro to construct and analyse decision-trees, Markov models and its extensions.
- Conduct budget impact analysis.
- Implement advanced modelling strategies such as individual-level simulations and distributional cost-effectiveness analysis
This course will require TreeAge Pro software.