The metafor Package

A Meta-Analysis Package for R

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Package News

July 17th, 2020: R Code for Meta-Analysis Books

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.

June 8th, 2020: Weights in Models Fitted with the rma.mv() Function

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.

May 27th, 2020: Computing Adjusted Effects Based on Meta-Regression Models

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.

May 9th, 2020: Modeling Non-Linear Associations in Meta-Regression

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.

March 31st, 2020: Interpreting Coefficients in Meta-Regression Models with (Log) Risk Ratios

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.

March 20th, 2020: Two New Functions for Network Meta-Analysis

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).

March 19th, 2020: News Version (2.4-0) on Its Way to CRAN

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.

news/news.txt ยท Last modified: 2020/07/17 19:37 by Wolfgang Viechtbauer