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--- news:news [2020/08/09 16:37] – Wolfgang Viechtbauer
+++ news:news [2020/10/14 09:17] – Wolfgang Viechtbauer
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 ~~NOTOC~~
+==== October 14th, 2020: Selection Models ====
+I've added the possibility to fit so-called 'selection models' to the metafor package. In case you are not familiar with such models: Selection models attempt to model and therefore account for the process by which the studies included in a meta-analysis may have been influenced by some form of publication bias. In other words, some kind of selection process may have happened that made it more likely that certain types of studies will be published and hence are more easily found and therefore can be included in a meta-analysis (yes, one should always search the 'gray literature' for unpublished studies to be included in a meta-analysis, but uncovering those studies lingering in some file drawers out there can be exceedingly difficult).
+The classical example of such a selection process is the fact that statistically significant findings are more likely to be submitted/accepted for publication. As a result, the findings from a meta-analysis can be biased, sometimes quite severely (because especially the smaller studies can only achieve statistical significance if they just happen to have obtained a large effect). Selection models attempt to correct for this (or can be used for sensitivity analyses by varying the degree of severity of such a selection process).
+To make this possible directly within the metafor package, I've added the [[https://wviechtb.github.io/metafor/reference/selmodel.html|selmodel()]] function, which provides a wide variety of selection model types (there are lots of proposals out there for how to model the selection process), including the 'beta selection model' by Citkowicz and Vevea (2017), a bunch of selection models suggested by Preston et al. (2004), an extension thereof that I call the 'negative exponential power selection model' (sounds fancy, huh?), and so-called 'step function models' as described by Iyengar and Greenhouse (1988), Hedges (1992), Vevea and Hedges (1995), and Vevea and Woods (2005). I wrote the code so that it would be relatively easy to add further selection models to the function in case further models end up being suggested in the statistical literature.
+Note that the [[https://cran.r-project.org/package=weightr|weightr]] package can also fit step function models and some other selection models are implemented in the [[https://cran.r-project.org/package=metasens|metasens]] and [[https://cran.r-project.org/package=selectMeta|selectMeta]] packages.
 ==== August 9th, 2020: R Code for Even More Meta-Analysis Books ====