Wolfgang Viechtbauer

Marginally significant (p = .07)

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The metafor Package

If you are looking for information about the metafor package for R, please go to the package website.

Google Scholar

A list of my publications and citation information/indices can be found on Google Scholar.

Cross Validated

Occasionally, I contribute to Cross Validated, a question and answer website for people interested in statistics, machine learning, data analysis, data mining, and data visualization (link to my profile).


Meta-Analysis Course

General Information

Course Dates to be announced
Course Location Maastricht, The Netherlands (see details below)
Registration Deadline to be announced
Course Fee see below

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: Despite the level of detail, this schedule is tentative. The starting and ending times of the course are definite, but everything in between is subject to change. Also, breaks are not explicitly indicated in the schedule below, but are planned in throughout the days.

Day 1
09:00-09:15 Introduction
09:15-10:45 Lecture: Introduction to systematic reviews and meta-analysis
10:45-12:00 Lecture: Outcome measures for meta-analysis
12:00-13:00 Lunch
13:00-14:00 Exercise 1
14:00-15:30 Lecture: The meta-analytic equal- and random-effects models
15:30-16:30 Lecture: Meta-analysis with R
17:00-18:00 Exercise 2 (part a)
Day 2
09:00-10:30 Lecture: Conditional vs. unconditional inferences (equal/fixed/random-effects models)
10:30-12:00 Lecture: Moderator analysis (meta-regression and subgrouping)
12:00-13:00 Lunch
13:00-14:00 Exercise 2 (part b)
14:00-15:00 Lecture: Quantifying and examining heterogeneity
15:00-16:00 Exercise 2 (part c)
16:00-17:00 Lecture: Model diagnostics (residuals, outliers, influential studies)
17:00-18:00 Exercise 2 (part d)
Day 3
09:00-10:30 Lecture: Publication bias
10:30-11:30 Exercise 3
11:30-12:00 Lecture: Refined tests and CIs for random/mixed-effects models
12:00-13:00 Lunch
13:00-14:30 Lecture: Multilevel, multivariate, and network meta-analysis
14:30-16:00 Lecture: A mixed bag of other topics and final Q&A session
16:00-16:30 Some literature suggestions

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 practitioner 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 on R). 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.

What to Bring to the Course

We will conduct a number of practical exercises throughout the course that require a computer. Therefore, please bring a laptop to the course. Moreover, you should have the current version of R installed on the laptop (R is a software environment for statistical computing and graphics). More information about R can be found at the R Project Website. You can download R from the Comprehensive R Archive Network (CRAN) (if you use Windows, choose "Download R for Windows", then "base", and then download the setup program; versions for MacOS and Linux are also available).

Once R is installed, please 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. If you have an internet connection, you should be able to install the package by starting R and then typing install.packages("metafor") (when prompted to choose a mirror, select the default option or a location that is nearby).

Software to open/view PDF files should also be installed on the laptop (e.g., Adobe Reader or see Wikipedia for a comprehensive list of PDF software). In all likelihood, such software is already installed on your laptop anyway.

You can also pair up with another course participant for the computer exercises, so technically not everybody needs to bring a laptop. However, if at all possible, please bring your own laptop so we don't end up with the unfortunate situation that nobody brings one!

Course Fee

The course fee is 600 Euros and includes lunch on all three days, refreshments (e.g., coffee, tea, water) during the breaks, but not dinner or accommodations.

When the course is sponsored by the Interuniversity Graduate School of Psychometrics and Sociometrics (IOPS), then the course is free for IOPS PhD students.

Course Location

The course will be held at Hotel van der Valk, Maastricht, the Netherlands.

Hotel Location

Nijverheidsweg 35
6227 AL Maastricht
The Netherlands
Google Maps Link

Arriving by Car

From the A2/E25, take exit N278. Turn left at the traffic lights in the direction "Cadier en Keer, Vaals". Then turn right into 1 Juliweg and again right into Nijverheidsweg. The parking lot of the hotel is immediately to your left.

Arriving by Train

If you arrive by train, you have a couple options. If you leave the train at the main station in Maastricht, then you can take bus number 4 in the direction "AZM (Academisch Ziekenhuis)" (the bus leaves twice every hour, at 17 and at 47 minutes past the hour). Take the bus to the Nijverheidsweg stop (takes about 8 minutes). The hotel is right down the road from the bus stop (100m).

Alternatively, if you leave the train at Randwyck train station, you can take a leisurely stroll as shown on the map below (approximately 10 minutes). You can also take bus number 4 in the direction "Villapark" and again exit at the Nijverheidsweg stop.

For a map corresponding to these instructions, click here.

Hotel Accommodations

If you need hotel accommodations, the most convenient place to stay will be at Hotel van der Valk, which is also the course location. In general, a useful website for hotel accommodations in and around Maastricht can be found here.

Miscellaneous Information

Instructional Language English
Min/Max Number of Participants 10/25
Certificate for Participation Yes
Number of European Credit Points 1
course_ma.txt · Last modified: 2017/02/06 16:30 by Wolfgang Viechtbauer