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This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being

Academic Year 2019/2020

Learning outcomes

The course aims to introduce students to fundamentals in economic evaluation theory and modeling methodology in healthcare. Students will learn how to structure a medical decision model and how to estimate the incremental cost-effectiveness (ICER) and cost-effectiveness acceptability curves (CEACs), how to interpret results and how to draw policy implications. Students are also trained in critically reviewing HTA-studies, in order to judge their validity and applicability.

Course contents

By the end of this course, students will gain knowledge of:

1. CEA/CBA analysis;

2. Uncertainty and decision-making in healthcare;

3. Fundamental concepts of economic evaluation theory;

4. Theoretical foundation of modeling;

5. Types of models;

6. Development of decision trees and Markov models;

7. Sensitivity analysis and the interpretation of the results.

Students will learn how to:

1. Define a decision problem and translate it into a decision analytic model;

2. Develop decision trees in excel;

3. Develop Markov models in excel;

4. Undertake deterministic sensitivity analysis;

5. Use results from clinical trials and other data sources as inputs in models.


* Course outline

Lecture 1. Introduction to economic evaluation;

The role of welfare economic in the economical evaluation context (cost-effectiveness / cost-benefit analysis). Introduction to efficiency and market failure.

Lecture 2. Assessment of economic evaluation;

Useful set of guidelines to evaluate EE studies. Covering the information that you need to judge if a study has the right methodology and to which decision-making context the results are applicable.

Lecture 3. Fundamental of economical evaluation – Cost Analysis;

Cost definitions and rules for thinking about which costs to include or exclude. Future costs and discounting.

Lecture 4. Fundamental of economical evaluation - CEA;

CEA: Measuring health benefits in terms of natural units including time (the survival analysis model).

Lecture 5. Fundamental of economical evaluation - CUA;

QALYs and cost-utility analysis, evaluation of preferences and the utility estimations (standard gambling and the EQ-5D) – Part 1.

Lecture 6. Fundamental of economical evaluation - CUA;

QALYs and cost-utility analysis, evaluation of preferences and the utility estimations (standard gambling and the EQ-5D) – Part 2.

Lecture 7. Fundamental of modeling in health care 1;

Excel class 1: The Fundamentals and development of decision tress modeling.

Lecture 8. Fundamental of modeling in health care 2;

Excel class 2: Fundamentals and development of Markov model 1.

Lecture 9. Fundamental of modeling in health care 3;

Excel class 3: Development of Markov model 2 (Sensitivity analysis)

Lecture 10. 20TH May 2019. Sum-up & questions session

Teaching methods

The course consists of ten classes. Classes are lectures which last three hours, with breaks where necessary; 3 Classes at least will be Excel workshops carried out in the computer lab.


The course requires a reasonable level of understanding of basic mathematical concepts and basic probability theory. Students should also be comfortable with the basics of spreadsheet software such as MS Excel.

Assessment methods

Grading will be based on a combination of two written exam and case presentation.

The grading percentages to be assigned later.

Teaching tools

During the course, students will work with simulated data sets from published studies.

These will be used to the development of the decision trees and Markov models.

Some exercises are included in some lectures. These are to be completed in class or at home.

Office hours

See the website of Abdul Jabbar Omar Alsaleh