This course will enable students to explore the world of systematic reviews and meta-analyses. By combining the results of multiple interventional trials using meta-analytic models, students will be able to understand the significance of these result models in health decisions. By the end of this course, students will have grasped the major methodological and statistical points of meta-analyses, will be able to run simple models on R Studio, and will be capable of critically interpreting the results of a meta-analysis.
Objective 1: Understand the foundations of meta-analysis
By the end of this course, students will gain a comprehensive understanding of the principles and significance of systematic reviews and meta-analyses.
Objective 2: Master methodological aspects of meta-analyses
Equip students with the necessary skills to conduct a meta-analysis by guiding them through the steps involved and emphasizing methodological requirements. Students will also develop proficiency in key statistical aspects such as effect size, weight, pooling of effect size, heterogeneity, sub-group analyses, sensitivity analyses, meta-regression, and identification of publication bias.
Objective 3: Recognize the role of meta-analyses in health decision-making
Enable students to critically interpret the results of a meta-analysis and appreciate the pivotal role of meta-analyses in informing health decisions. By the end of the course, students should be able to articulate the importance of meta-analytic models in the broader context of evidence-based healthcare.
Objective 4: Introduce and navigate statistical software for meta-analysis
Introduce students with R studio statistical software used in the field of meta-analysis.
Introduction to meta-analysis ; steps to conduct a meta-analysis ; methodological requirements for the good conduct of a meta-analysis; statistical aspects of meta-analysis (effect size, weight, pooling of effect size, heterogeneity, sub-group analyses, sensitivity analyses, meta-regression, publication bias); aggregate data vs individual data ; introduction to network meta-analysis; the importance of meta-analyses in the panel of evidence ; the importance of meta-analyses in healthcare decisions ; limitations of meta-analysis; introduction to statistical software for the conduct of meta-analyses.
Different result interpretation exercises will be carried out in class.
Students will also be required to complete exercises at home.
Finally, practical sessions with exercises on R Studio will be organised.
A course book will be provided to students at the beginning of the course; it will include an introduction, the course structure, the educational format, the schedule, the course objectives, the examination modalities, the language of instruction, and the professor’s contact information.
Oral examination
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Master in Biomedical Sciences | Standard | 0 | 3 | |
| Master in Biomedical Sciences, Professional focus in Preclinical Research | Standard | 0 | 3 | |
| Master in Biomedical Sciences, Professional focus in Clinical Research | Standard | 0 | 3 | |
| Master in Biomedical Sciences | Standard | 1 | 3 | |
| Master in Biomedical Sciences, Professional focus in Preclinical Research | Standard | 2 | 3 | |
| Master in Biomedical Sciences, Professional focus in Clinical Research | Standard | 2 | 3 |