Meta-analysis encompasses an array of statistical methods for aggregating and comparing the results from related studies in a systematic manner. The focus of this workshop is on standard and advanced methods and techniques for analyzing meta-analytic data.
We will start out by reviewing standard meta-analytic models (i.e., fixed-, random-, and mixed-effects meta-regression models). 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, and network meta-analysis / mixed treatment comparisons (MTCs). We will also examine the distinction between models that use the normal distribution as an (asymptotic) approximation to the sampling distribution of the observed outcomes and models that are based on other distributional assumptions (i.e., binomial and Poisson distributions), which then lead to random/mixed-effects (conditional) logistic and Poisson regression models.
The workshop consists of a series of lectures and practical exercises. All analyses will be conducted in R using the metafor package, so participants are kindly asked to bring their own laptop with R (and optionally RStudio) installed. A basic understanding of the formal steps of a meta-analysis is useful.
The workshops will take place on Sunday, 15.09.2019 from 9:00 to 17:00. Registration for the workshops can be completed via ConfTool.