The R code for two more books has been added to the GitHub repo: The Handbook of Research Synthesis and Meta-Analysis by Cooper et al. (2019) and Publication Bias in Meta-Analysis by Rothstein et al. (2005).
I've started a repo on GitHub to provide R code for various books on meta-analysis. It now contains Introduction to Meta-Analysis by Borenstein et al. (2009) and Practical Meta-Analysis by Lipsey and Wilson (2001). More to be added. The items in the repo will also be listed under the Analysis Examples section.
And another entry to the 'Tips and Notes' section, this time discussing how weighting works in more complex models, such as those that can be fitted with the
rma.mv() function. You can read the tutorial here.
I've added an entry to the 'Tips and Notes' section, discussing how to compute 'adjusted effects' based on meta-regression models. You can read the tutorial here.
I've added an entry to the 'Tips and Notes' section, illustrating how to model non-linear associations in meta-regression using polynomial and restricted cubic spline models. You can read the little tutorial here.
Based on a question I received, I wrote up a little tutorial on how to interpret the coefficients in meta-regression models when using the log risk ratio as the outcome measure. When exponentiating coefficients, this leads to values that represent ratios of risk ratios, which may not be entirely obvious. You can read the tutorial here.
As a follow-up to yesterday's note, it is maybe worth mentioning that I also added two functions that are especially useful for those conducting network meta-analyses with the metafor package. With the
to.wide() function, one can rearrange a dataset that is in an arm-based 'long' format to a contrast-based 'wide' format. Two examples illustrating the use of this function can be found under help(to.wide) (the link takes you to the corresponding help file, which is nicely formatted and shows the output of the examples). Once the dataset is in such a wide format, an important next step is the construction of variables that reflect which two groups are being compared with each other in each row (through +1, 0, -1 coding). Such a contrast matrix can be easily created with the
contrmat() function. See help(contrmat) for the help file and two examples illustrating its use. The analysis of these two datasets (using arm- and contrast-based models) are illustrated under help(dat.hasselblad1998) and help(dat.hasselblad1998).
Just submitted a new version (2.4-0) to CRAN. This update was prompted by the upcoming change in R where the new default will be
stringsAsFactors=FALSE (at long last!). As a result, some tests were failing on R-devel, so these needed fixing. Along the way, I made various minor internal updates and added some convenience functionality to several functions. The full changelog can be found here.