Description

A meta-analysis enables the PhD-candidate:

1. to obtain a good overview of the state-of-the art of the research field of the PhD-project, and
2. to get insight in the way the research problem is tackled, analysed and reported in existing literature.  

In this two-day course, the core issues of meta-analysis are explained and practiced.

Programme

• The first day of the course is targeted at the basic concepts (measures of effect size, heterogeneity, publication bias, trim and fill method, etc.).  Pitfalls in the data collection and coding will also be tackled (e.g., problems with retrieving studies, analysing multiple outcome measures, multiple treatment arms, multiple follow-up moments, different measures etc.). In addition, the participants perform basic meta-analyses in the programme R (R-package metafor; Viechtbauer, 2002) on example data sets. Some exercises can also be performed in Comprehensive Meta-Analysis (Borenstein, 2009).
   
• The second day is targeted at more advanced aspects of meta-analysis (meta-regression with multiple moderators, multilevel meta-analysis, interactions between moderators, meta-CART) and new developments in meta-analysis. 

Each day consists of a mixture of lectures and hands-on exercises.

Course objectives

After this course, you are able to:

• Understand and explain the basic concepts of meta-analysis: measures of effect-size, fixed-effect  and random-effects models, (methods to assess) publication bias, forest plot.
• Understand and explain more advanced topics: methods to assess and explain effect size heterogeneity, meta-regression, multilevel meta-analysis, and meta-CART.
• Perform in R: basic meta-analysis, (multilevel) meta-regression, publication bias analysis, forest plot,  and meta-CART. 

Reading list

• Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Chichester:  Wiley & Sons.
• Assink, M., & Wibbelink, C. J. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154-174.
• Li, X., Dusseldorp, E., Su, X., & Meulman, J. J. (2020). Multiple moderator meta-analysis using the R-package Meta-CART. Behavior Research Methods, 52(6), 2657-2673.
• Other articles to be announced.
   

Prerequisites

• Basic knowledge of R (e.g., online course coursera: “Getting started with R”);
• Bring your own laptop with R (and R-studio) (and optionally CMA);
• Those who are more experienced in meta-analysis may skip the first day of this course. If in doubt, please contact the coordinator.