# Wolfgang Viechtbauer

Marginally significant (p = .07)

course_ma

## Meta-Analysis Course

### General Information

 Course Dates to be announced Course Times to be announced Course Location online Registration Deadline to be announced Course Fee to be announced

### Course Description

Researchers trying to summarize the constantly growing body of research in the social, health, and natural sciences are increasingly using a technique called meta-analysis. Meta-analysis encompasses an entire array of statistical methods for aggregating and comparing the results from related studies in a systematic manner. For example, meta-analysis is frequently used to determine whether a particular treatment or intervention is actually effective overall and whether the effectiveness of the treatment or intervention depends on certain study and/or subject characteristics (so-called moderator variables). The focus of this course will be on current methods and techniques for analyzing meta-analytic data.

We will start out with a short overview of the entire meta-analytic process (consisting of seven steps: problem formulation, literature search, information gathering, quality evaluation, analysis, interpretation of findings, and presentation of results). Next, we will examine how the results from a study can be summarized with various effect size or outcome measures. We will then delve into equal-, fixed-, and random/mixed-effects models for combining the observed outcomes and for examining whether the outcomes depend on one or more moderator variables. The use of so-called meta-regression models will be emphasized in this context. Model diagnostics and methods for sensitivity analyses will be covered as well.

A major problem that may distort the results of a meta-analysis is publication bias (the fact that the published literature may not be representative of all the research that has been conducted on a particular topic). Therefore, current methods for detecting and dealing with publication bias will be discussed next. Finally, time permitting (and depending on the interests of the participants), we will examine missing data issues, sequential/cumulative methods in the context of meta-analysis, meta-analytic techniques using individual subject data, multilevel and multivariate models, methods for dealing with dependent/correlated outcomes, and Bayesian approaches to meta-analysis.

The course consists of a mixture of lectures, hands-on tutorials, and computer exercises to cover not only the theoretical background, but also provide practical experience with analyzing real meta-analytic datasets. Emphasis throughout the course is on the application of the various methods and the interpretation of the results obtained (supplementary references can be provided to those interested in the mathematical/statistical details).

### Course Schedule

Note: Given the dynamics of a live and interactive course, this schedule is tentative. Also, while the starting time for each day is definite (i.e., 16:00 my local time), the ending times can vary a bit (I would say ±30 minutes). Also, breaks are not indicated in the schedule, but we will take them as needed.

Day 1
16:00-16:30 Introduction / Logistics
16:30-18:00 Lecture: Introduction to systematic reviews and meta-analysis
18:00-19:30 Lecture: Outcome and effect size measures for meta-analysis
19:30-20:30 Exercise 1
20:30-22:00 Lecture: The meta-analytic equal- and random-effects models
Day 2
16:00-17:00 Lecture: Meta-analysis with R and the metafor package
17:00-18:00 Exercise 2 (part a)
18:00-19:00 Lecture: Conditional vs. unconditional inferences (equal/fixed/random-effects models)
19:00-20:30 Lecture: Moderator analysis (meta-regression and subgrouping)
20:30-22:00 Exercise 2 (part b)
Day 3
16:00-17:00 Lecture: Quantifying and examining heterogeneity
17:00-18:00 Exercise 2 (part c)
18:00-19:00 Lecture: Model diagnostics (residuals, outliers, influential studies)
19:00-20:00 Exercise 2 (part d)
20:00-21:30 Lecture: Publication bias
21:30-22:30 Exercise 3
Day 4
16:00-16:30 Lecture: Refined tests and CIs for random/mixed-effects models
16:30-18:00 Lecture: Multilevel, multivariate, and network meta-analysis
18:00-19:30 Exercise 4
19:30-21:30 Lecture: A mixed bag of other topics
21:30-22:00 Final Q&A session

### Course Prerequisites

In general, all efforts will be made to make the course as self-contained as possible. However, there are a couple things one can do to prepare for the course.

First of all, although all aspects of the entire meta-analytic process will be discussed, emphasis will be placed on the analysis and interpretation step of a meta-analysis. Therefore, a general familiarity with how meta-analyses are conducted will help to provide a better understanding of the course contents (reading a few meta-analyses from one's field of interest would be sufficient to obtain a general impression).

Second, some basic knowledge of statistical methods at the undergraduate level (e.g., regression, analysis of variance, hypothesis testing) will also be beneficial. Also, matrix algebra notation will occasionally be used to present certain equations. However, if you are not familiar with matrix algebra, then this is not a problem. We will use software anyway to carry out the computations and the most important aspect for the applied researcher is the interpretation of the results (which will be covered in much detail).

Finally, the primary software package to be used during the course for the analyses will be R (see below for more details). Although everything that you need to know to do the computer exercises will be explained in the course, it would be useful to familiarize yourself with R a little bit ahead of time (here are some notes on preparing to use R). You could also consider following the Intro to R Course first.

### How to Prepare for the Course

The course will be given online via the video conferencing platform / software Zoom. While it is possible to join the course simply via your browser, I would not recommend this (certain features are not available via the 'web client' and the video/audio quality tends to be poorer). Therefore, I would highly recommended to install the Zoom client (which you can get here).

Although not strictly necessary, it will be useful to also install an integrated development environment (IDE) for R. A popular choice these days is RStudio. So, unless you already have a different setup, please download the appropriate installer of RStudio for your OS from here and install this in the usual manner.

Once R and RStudio are installed, please also install the metafor package (an add-on for conducting meta-analyses with R). More details about the package can be found at the metafor package website. You should be able to install the package by starting RStudio and then typing install.packages("metafor") into the 'Console'.

### Course Registration

To register for the course, please complete the registration form (to be posted) and send it by e-mail to:

Jolanda Koch
Department of Psychiatry and Neuropsychology
Maastricht University
P.O. Box 616 (VIJV1)
6200 MD Maastricht
The Netherlands

Tel: +31 (43) 388-3511
E-mail: jolanda.koch (at) maastrichtuniversity.nl

We will notify you upon receipt of the form whether there are still open places for the course. If open places are available, then you can complete your registration by paying the registration fee (as described in the registration form).

### Miscellaneous Information

 Instructional Language English Min/Max Number of Participants 10/50 Certificate for Participation Upon request Number of European Credit Points 1