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 300 Euros

Note: If you would like to be notified once arrangements for a new round of the course have been made, please send a mail to wvb (at) wvbauer (dot) com to indicate your interest and I would be happy to send you a mail once there is an update (this way, you do not have to keep checking this website).

Course Description

Meta-analysis denotes a particular approach to research synthesis that makes use of quantitative methods for aggregating and comparing the results from related studies in a systematic manner and has become the method of choice for summarizing the constantly growing body of research in the social, health, and natural sciences. The focus of this course is on current methods and techniques for conducting a meta-analysis.

We will start out by discussing the meta-analytic process as a whole (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 individual studies can be quantified in terms of various effect size or outcome measures (e.g., raw or standardized mean differences, ratios of means, risk/odds ratios, risk differences, correlation coefficients). We will then delve into methods for combining the observed outcomes (i.e., via equal- and random-effects models) and for examining whether the outcomes depend on the characteristics of the studies from which they were derived (i.e., via meta-regression and subgrouping). Methods for quantifying heterogeneity, model diagnostics, and for conducting sensitivity analyses will be covered as well.

A major problem that may distort the results of a meta-analysis is publication bias (i.e., when the studies included in a meta-analysis are not 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, we will examine some specialized methods for meta-analyzing 2x2 table data (the Mantel-Haenszel method, Peto's method, and logistic mixed-effects models), for meta-analyzing the results of regression models, and then cover a mixed bag of other topics (e.g., cumulative meta-analysis, missing data, Bayesian models).

The course consists of a mixture of lectures, hands-on tutorials, and computer exercises and provides 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. References will be provided to those interested in further 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, the ending times can vary a bit (by ±30 minutes). Also, shorter breaks are not indicated in the schedule, but we will take them as needed.

Day 1
09:00-09:30 Introduction / Logistics
09:30-11:30 Lecture: Introduction to systematic reviews and meta-analysis
11:30-12:30 Lecture: Outcome and effect size measures for meta-analysis
12:30-13:30 Lunch / Break
13:30-14:30 Exercise 1
14:30-16:00 Lecture: The meta-analytic equal- and random-effects models
16:00-17:00 Lecture: Meta-analysis with R and the metafor package
17:00-18:00 Exercise 2 (part a)
Day 2
09:00-10:30 Lecture: Moderator analysis (meta-regression and subgrouping)
10:30-11:30 Exercise 2 (part b)
11:30-12:30 Lecture: Quantifying and examining heterogeneity
12:30-13:30 Lunch / Break
13:30-14:30 Exercise 2 (part c)
14:30-15:30 Lecture: Model diagnostics (residuals, outliers, influential studies)
15:30-16:30 Exercise 2 (part d)
16:30-17:30 Lecture: Refined tests and CIs for random/mixed-effects models
Day 3
09:00-10:30 Lecture: Publication bias
10:30-11:30 Exercise 3
11:30-12:30 Lecture: Methods for meta-analyzing 2x2 table data
12:30-13:30 Lunch / Break
13:30-14:30 Exercise 4
14:30-15:30 Lecture: Methods for meta-analyzing regression models
15:30-16:30 Lecture: A mixed bag of other topics
16:30-17:30 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. 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 is usually sufficient to obtain a general impression).

Second, basic knowledge of statistical methods (e.g., regression, analysis of variance, hypothesis testing) is assumed. 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 great detail).

Finally, the primary software package to be used for the analyses during the course 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, this is not a comprehensive R programming course. Therefore, if you are new to R, it would be useful to familiarize yourself with R a little bit ahead of time (see also my notes on preparing to use R).

How to Prepare for the Course

The course will be given online via the video conferencing platform 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 recommend 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 and install RStudio.

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 on the metafor package website. You should be able to install the package by starting R/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 later) 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 so, then you can complete your registration by paying the registration fee (instructions will be provided via e-mail).

Miscellaneous Information

 Instructional Language English Min/Max Number of Participants 10/75 Certificate for Participation Upon request Number of European Credit Points 1
course_ma.txt · Last modified: 2021/08/16 10:26 by Wolfgang Viechtbauer