View source: R/runMetaAnalysis.R
runMetaAnalysis  R Documentation 
This wrapper function allows to simultaneously pool effect sizes using different metaanalytic approaches.
runMetaAnalysis(data, which.run = c("overall", "combined", "lowest.highest", "outliers", "influence", "rob", "threelevel", "threelevel.che"), method.tau = "REML", hakn = TRUE, study.var = "study", extra.grouping.var = NULL, arm.var.1 = "Cond_spec_trt1", arm.var.2 = "Cond_spec_trt2", measure.var = "Outc_measure", es.var = "es", se.var = "se", low.rob.filter = "rob > 2", method.tau.ci = "QProfile", round.digits = 2, which.outliers = c("general", "combined"), which.influence = c("general", "combined"), which.rob = c("general", "combined"), nnt.cer = 0.2, rho.within.study = 0.5, use.rve = TRUE, html = TRUE, ...)
data 

which.run 

method.tau 

hakn 

study.var 

extra.grouping.var 

arm.var.1 

arm.var.2 

measure.var 

es.var 

se.var 

low.rob.filter 

method.tau.ci 

round.digits 

which.outliers 

which.influence 

which.rob 

nnt.cer 

rho.within.study 

use.rve 

html 

... 
Additional arguments. 
The runMetaAnalysis
function is a wrapper for several types of metaanalytic models
that are typically used. It allows to run all of these models in one step in order to generate results
that are somewhat closer to being "publicationready".
By default, the following models are calculated:
"overall"
. Runs a generic inversevariance (randomeffects) model. All included
effect sizes are treated as independent.
"combined"
. Pools all effect sizes within one study (defined by study.var
) before
pooling. This ensures that all effect sizes are independent (i.e., unitofanalysis error &
doublecounting is avoided). To combine the effects, one has to assume a correlation of effect sizes
within studies, empirical estimates of which are typically not available.
"lowest.highest"
. Runs a metaanalysis, but with only (i) the lowest and (ii) highest
effect size within each study included.
"outlier"
. Runs a metaanalysis without statistical outliers (i.e. effect sizes for which
the confidence interval does not overlap with the confidence intervall of the overall effect).
"influence"
. Runs a metaanalysis without influential cases (see influence.rma.uni
for
details).
"rob"
. Runs a metaanalysis with only lowRoB studies included.
"threelevel"
. Runs a multilevel (threelevel) metaanalysis model, with effect sizes nested
in studies.
"threelevel.che"
. Runs a multilevel (threelevel) metaanalysis model, with effect sizes nested
in studies. Variancecovariance matrices of each study with two or more effect sizes are estimated using
rho.within.study
as the assumed overall withinstudy correlation. This imputation allows to run a "correlated and
hierarchical effects" (CHE) model, which is typically a good approximation for data sets with unknown and/or
complex dependence structures.
For more details see the help vignette: vignette("metapsyTools")
.
Returns an object of class "runMetaAnalysis"
. This object includes, among other things,
a data.frame
with the name summary
, in which all results are summarized  including the
studies which were removed for some analysis steps. Other objects are the "raw" model objects returned
by all selected analysis types. This allows to conduct further operations on some models specifically (e.g.
run a metaregression by plugging one of the model objects in update.meta
.
Mathias Harrer mathias.h.harrer@gmail.com, Paula Kuper paula.r.kuper@gmail.com, Pim Cuijpers p.cuijpers@vu.nl
calculateEffectSizes
## Not run: data("inpatients") library(meta) inpatients %>% checkDataFormat() %>% checkConflicts() %>% expandMultiarmTrials() %>% calculateEffectSizes() > data # Run the metaanalyses runMetaAnalysis(data) > res # Show summary res # Show forest plot (by default, "general" is used) plot(res) # Show forest plot of specific analysis plot(res, "outliers") plot(res, "threelevel") plot(res, "baujat") plot(res, "influence") plot(res, "lowest.highest") # Extract specific model and do further calculations # (e.g. metaregression on 'year') metareg(res$model.overall, ~year) # For the combined analysis, use provide an extra variable # so that different treatment arm comparisons in multiarm trials are NOT pooled data("psyCtrSubsetWide") psyCtrSubsetWide %>% checkDataFormat() %>% checkConflicts() %>% expandMultiarmTrials() %>% calculateEffectSizes() %>% filterPoolingData(year > 2010) %>% runMetaAnalysis(extra.grouping.var = "Cond_spec_trt1") ## End(Not run)
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