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- The estimation of the effect sizes now relies on the metaConvert
package.
- The GRADE criteria have been reworked in collaboration with a large
team with extensive experience in evidence synthesis
- Correct some bugs, including an error that could appear in the
Egger’s test if ‘outcome’ instead of ‘outcomes’ was included in the
mutiple_es colmun (github issue).
- Rework importation of the forest function from the meta package
- Redesign of the forest.umbrella function (now a wrapper of the
amazing forest.meta function from the meta package). This also allows to
plot R as effect size measures, and to correct some bugs that could
appear when using “eOR” in v1.0.8.
- The add_evidence function can now stratify the evidence according to
Ioannidis criteria for R and Z measures if users enter the number of
cases
- Fixed an issue that could appear when a meta-analysis presented
multiple outcomes with a correlation as effect size measure
- Added a groove() function to visualize overlap of primary studies
(inspired by the work of Pérez-Bracchiglione et al. 2022)
- Fixed some bugs preventing to use all functions in the app and
jamovi module
- Three new effect size measures are available (SMC, R, and Z)
- Three new ESB tests are available (from Stanley and colleagues,
2021: PSST, TESS, TESSPSST)
- Fixed some bugs when drawing a forest plot with colors
- The summary() function now allows to obtain more information on
heterogeneity (via the ‘het_max’ argument)
- The umbrella() function now allows to request a fixed-effect
meta-analysis (via the ‘method.var’ argument)
- The umbrella() function now allows to choose the excess of
statistical significance test (via the ‘method.esb’ argument)
- The unrestricted weighted least squares weighted average can now be
used to estimate the best approximation of the true effect (via the
‘true_effect’ argument of the umbrella() and esb.test() functions)
- The add.evidence() function now warns users when an issue occurred
when estimating the ESB test (a warning message is thus printed when
using the umbrella() + add.evidence() functions).
- New features are available in the forest.umbrella() function
- Test section: some tests have been updated
- umbrella() function: improvement of the meta-analysis selection
process based on user input
- Test section: several tests have been added
- Test section: made a rounding correction and explicitly calls the
estimator for the amount of heterogeneity (method.tau argument) when
using the functions of the meta package
- Documentation: made some improvements (correction of typos and
clarifications)
- First version released on CRAN
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.