Table of Contents
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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).
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.
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.
|Introduction / Logistics
|Lecture: Introduction to systematic reviews and meta-analysis
|Lecture: Outcome and effect size measures for meta-analysis
|Lunch / Break
|Lecture: The meta-analytic equal- and random-effects models
|Lecture: Meta-analysis with R and the metafor package
|Exercise 2 (part a)
|Lecture: Moderator analysis (meta-regression and subgrouping)
|Exercise 2 (part b)
|Lecture: Quantifying and examining heterogeneity
|Lunch / Break
|Exercise 2 (part c)
|Lecture: Model diagnostics (residuals, outliers, influential studies)
|Exercise 2 (part d)
|Lecture: Refined tests and CIs for random/mixed-effects models
|Lecture: Publication bias
|Lecture: Methods for meta-analyzing 2x2 table data
|Lunch / Break
|Lecture: Methods for meta-analyzing regression models
|Lecture: A mixed bag of other topics
|Final Q&A session
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).
We will conduct a number of practical exercises throughout the course where we make use of the statistical software package R. Therefore, please also download R from the Comprehensive R Archive Network (CRAN). Choose the appropriate "Download R" link depending on your operating system and follow the instructions for downloading and installing R. If you already have R installed, please check that it is the current version (you can check what the 'latest release' of R is by going to CRAN and then compare this with the version shown when you start R). If you do not have the latest version installed, please update.
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'.
At this time, it is not possible to register for the course. The course registration form will be posted as soon as another round of the course is organized.
Notes / FAQs
- The starting time of the course was chosen so that people from Europe, Africa, and Asia (and possibly Australia) can participate in the course without having to be up in the middle of the night (although people far East will need to be up quite late). My apologies to people from North/South America for whom these times are probably not feasible (a future round of the course might run at times more suitable for those from these regions).
- In the 'in person' courses that I teach, I often end up troubleshooting general computer problems for some of the course participants. I will not be able to do this in this course. Therefore, make sure you sort out any problems with installing the necessary software ahead of time.
- The course fee is meant to be paid per person. While I won't take active measures to check if a single person is behind each connection during the course, I appeal to your sense of fairness to register individually.
- The course will not be recorded. By registering, you also agree not to make any recordings of the course on your end.
|Min/Max Number of Participants
|Certificate for Participation
|Number of European Credit Points