|Course Date||to be announced|
|Course Times||to be announced|
|Course Location||to be announced|
|Registration Deadline||to be announced|
|Course Fee||to be announced|
The purpose of this course is to cover advanced methods for meta-analysis with particular emphasis on using the statistical software R. Therefore, course participants are expected to have an understanding of standard meta-analytic techniques and methodology (see below for course prerequisites).
We will start out by briefly reviewing standard meta-analytic models (i.e., equal/fixed-effects, random-effects, and meta-regression models), focusing on how to fit these models in R. This will then be the starting point to delve into more complex data structures that one may encounter in practice and to consider appropriate models for analyzing such data. Models and techniques to be discussed in this context include multilevel and multivariate models, methods for dealing with dependent/correlated outcomes, network meta-analysis (or what is also known as multiple/mixed treatment comparisons), models with crossed random effects, phylogenetic meta-analysis, and spatio-temporal models.
Course participants are expected to have an understanding of standard meta-analytic methods and ideally some prior experience with analyzing meta-analytic data. Most importantly, participants should be familiar with the computation and interpretation of outcome measures commonly used in meta-analyses (e.g., raw or standardized mean differences, ratios of means, risk/odds ratios, risk differences, raw or Fisher's r-to-z transformed correlations) and the interpretation and application of equal/fixed-effects, random-effects, and meta-regression models. I also offer a meta-analysis course that covers these topics in great detail.
The primary software package to be used 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).
Finally, further below, you will find a list of references, which describe some of the models and methods that will be discussed during the course. If you have the time, feel very ambitious, and don't already know these papers in the first place, it would be ideal to at least skim through them ahead of time in preparation for the course (and to get a better idea of the course contents). But this is not a course prerequisites per se – all of the models and methods to be discussed will be introduced step by step during the course without assuming that you have already read each and every paper listed below.
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'.
In addition, we are likely to make use of a number of additional packages during the course. You should be able to install all of the needed packages at once with the following command (this make take a few moments to complete):
install.packages(c("lme4", "numDeriv", "minqa", "nloptr", "dfoptim", "ucminf", "optimParallel", "multcomp", "sp", "ape"))
To register for the course, please complete the registration form (to be posted) and send it by e-mail to:
Department of Psychiatry and Neuropsychology
P.O. Box 616 (VIJV1)
6200 MD Maastricht
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).
|Min/Max Number of Participants||10/50|
|Certificate for Participation||Upon request|
|Number of European Credit Points||1|
Begg, C. B., & Pilote, L. (1991). A model for incorporating historical controls into a meta-analysis. Biometrics, 47(3), 899–906. (link)
Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17(22), 2537–2550. (link)
Fernández-Castilla, B., Maes, M., Declercq, L., Jamshidi, L., Beretvas, S. N., Onghena, P., & Van den Noortgate, W. (2019). A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis. Behavior Research Methods, 51(3), 1286-1304. (link)
Gleser, L. J., & Olkin, I. (2009). Stochastically dependent effect sizes. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 357–376). New York: Russell Sage Foundation.
Hasselblad, V. (1998). Meta-analysis of multitreatment studies. Medical Decision Making, 18(1), 37–43. (link)
Ishak, K. J., Platt, R. W., Joseph, L., Hanley, J. A., & Caro, J. J. (2007). Meta-analysis of longitudinal studies. Clinical Trials, 4(5), 525–539. (link)
Jackson, D., White, I. R., & Riley, R. D. (2013). A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biometrical Journal, 55(2), 231–245. (link)
Jackson, D., Barrett, J. K., Rice, S., White, I. R., & Higgins, J. P. (2014). A design-by-treatment interaction model for network meta-analysis with random inconsistency effects. Statistics in Medicine, 33(21), 3639–3654. (link)
Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2(1), 61–76. (link)
Law, M., Jackson, D., Turner, R., Rhodes, K., & Viechtbauer, W. (2016). Two new methods to fit models for network meta-analysis with random inconsistency effects. BMC Medical Research Methodology, 16, 87. (link)
Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. Evolutionary Ecology, 26(5), 1253–1274. (link)
Salanti, G., Higgins, J. P. T., Ades, A. E., & Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17(3), 279–301. (link)
Senn, S., Gavini, F., Magrez, D., & Scheen, A. (2013). Issues in performing a network meta-analysis. Statistical Methods in Medical Research, 22(2), 169–189. (link)
Trikalinos, T. A., & Olkin, I. (2012). Meta-analysis of effect sizes reported at multiple time points: A multivariate approach. Clinical Trials, 9(5), 610–620. (link)
van Houwelingen, H. C., Zwinderman, K. H., & Stijnen, T. (1993). A bivariate approach to meta-analysis. Statistics in Medicine, 12(24), 2273–2284. (link)
van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21(4), 589–624. (link)
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1-48. (link)