A Practical Introduction to the Meta-Analysis Toolkit

About

Systematically synthesizing studies stands as a critical element in advancing knowledge and facilitating informed decision-making in public health practice, policy, and research. One important way to synthesize evidence is through meta-analysis. Meta-analysis is a statistical toolkit used to consolidate findings from multiple primary studies, allowing for a robust estimation of the pooled effect size and to assess the quantitative heterogeneity across available estimates. Thus, in this course, participants will learn how to conduct meta-analysis and interpret their results. Particular emphasis will be placed on meta-analytic methods to pool the quantitative evidence available, describing the different types of models available and how to assess quantitative heterogeneity (ie. variability of intervention effects across studies) including specific test of homogeneity, outlier detection, and meta-regression analysis. We will also examine methods to assess small studies effects, publication bias, and sensitivity analyses. We will learn how to interpret and report meta-analysis results including forest plots and graphical ways to detect small-study effects (publication bias). A basic introduction to the freely available software R will also be provided and used to perform meta-analysis and meta-regressions.

Course leader

Prof. Gian Luca Di Tanna, Dr. Joseph Alvin Ramos Santos, Emilia Riggi - PhD, Robert Grant

Course aim

y the end of the course participants will:
- Know the basic methods of the meta-analytic toolkit;
- Be able to describe and interpret the results of meta-analysis;
- Be able to perform a full meta-analysis using R.

Fee info