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--- news:news2020 [2021/08/22 21:20] – [March 20th, 2020: Two New Functions for Network Meta-Analysis] Wolfgang Viechtbauer
+++ news:news2020 [2021/11/05 08:48] – Wolfgang Viechtbauer
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 ==== November 14th, 2020: An Aggregate Function ====
-In many meta-analyses, multiple effect size estimates or outcomes can be extracted from the same study. Ideally, such structures should be analyzed using an appropriate multilevel/multivariate model as can be fitted with the ''rma.mv()'' function. However, there may occasionally be reasons for aggregating multiple effect sizes or outcomes belonging to the same study (or to the same level of some other clustering variable) into a single combined effect size or outcome. I've added an ''aggregate()'' function (or to be precise, an ''aggregate.escalc()'' method function) to the package for this purpose. You can read the documentation for this function (and see some examples illustrating its use) [[https://wviechtb.github.io/metafor/reference/aggregate.escalc.html|here]].
+In many meta-analyses, multiple effect size estimates or outcomes can be extracted from the same study. Ideally, such structures should be analyzed using an appropriate multilevel/multivariate model as can be fitted with the ''[[https://wviechtb.github.io/metafor/reference/rma.mv.html|rma.mv()]]'' function. However, there may occasionally be reasons for aggregating multiple effect sizes or outcomes belonging to the same study (or to the same level of some other clustering variable) into a single combined effect size or outcome. I've added an ''aggregate()'' function (or to be precise, an ''[[https://wviechtb.github.io/metafor/reference/aggregate.escalc.html|aggregate.escalc()]]'' method function) to the package for this purpose (see the documentation for some examples illustrating its use).
 ==== October 14th, 2020: Selection Models ====
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 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.
+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.
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 ==== 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 [[tips:weights_in_rma.mv_models|here]].
+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 ''[[https://wviechtb.github.io/metafor/reference/rma.mv.html|rma.mv()]]'' function. You can read the tutorial [[tips:weights_in_rma.mv_models|here]].
 ==== May 27th, 2020: Computing Adjusted Effects Based on Meta-Regression Models ====
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 ==== 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 [[https://wviechtb.github.io/metafor/reference/to.wide.html|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 [[https://wviechtb.github.io/metafor/reference/contrmat.html|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 [[https://wviechtb.github.io/metadat/reference/dat.senn2013.html|help(dat.senn2013)]] and [[https://wviechtb.github.io/metadat/reference/dat.hasselblad1998.html|help(dat.hasselblad1998)]].
+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 ''[[https://wviechtb.github.io/metafor/reference/to.wide.html|to.wide()]]'' function, one can rearrange a dataset that is in an arm-based 'long' format to a contrast-based 'wide' format (see the documentation for examples illustrating the use of this function). 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 ''[[https://wviechtb.github.io/metafor/reference/contrmat.html|contrmat()]]'' function (again, see the documentation for examples illustrating its use). The datasets ''[[https://wviechtb.github.io/metadat/reference/dat.senn2013.html|dat.senn2013]]'' and ''[[https://wviechtb.github.io/metadat/reference/dat.hasselblad1998.html|dat.hasselblad1998]]'' illustrate the use of these functions (using arm- and contrast-based models).
 ==== March 19th, 2020: New Version (2.4-0) on Its Way to CRAN ====