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This function allows to simultaneously conduct different subgroup analyses using runMetaAnalysis objects.


                 .which.run = .model$which.run[1],
                 .round.digits = 2,
                 .nnt.cer = NULL,
                 .tau.common = FALSE,
                 .html = TRUE)



An object of class "runMetaAnalysis", created by runMetaAnalysis.


<dplyr_data_masking>. A number of subgroup variables included in the original dataset provided to runMetaAnalysis, separated by commas.


The model in .model that should be used for the subgroup analyses. Uses the default analysis in .model if no value is specified by the user.


numeric. Number of digits to round the (presented) results by. Default is 2.


numeric. Value between 0 and 1, indicating the assumed control group event rate to be used for calculating NNTs via the Furukawa-Leucht method. If set to NULL (default), the value saved in .model is (re-)used.


logical. Should a common (TRUE) or subgroup-specific (FALSE) estimate of the between-study heterogeneity be calculated when analyzing the subgroups? FALSE by default. Note that subgroup analyses based on "multilevel" models automatically assume common heterogeneity estimates.


logical. Should an HTML table be created for the results? Default is TRUE.


Returns an object of class "subgroupAnalysis". This object includes, among other things, a data.frame with the name summary, in which all subgroup analysis results are summarized. Other objects are the "raw" subgroup analysis model objects returned. This allows to conduct further operations on some subgroup analysis specifically.


For more details see the Get Started vignette.

See also


Mathias Harrer mathias.h.harrer@gmail.com, Paula Kuper paula.r.kuper@gmail.com, Pim Cuijpers p.cuijpers@vu.nl


if (FALSE) {

depressionPsyCtr %>%
  checkDataFormat() %>%
  checkConflicts() %>%
  calculateEffectSizes() %>% 
  filterPoolingData(condition_arm2 %in% 
                      c("wl", "other ctr")) -> data

# Run the meta-analyses
runMetaAnalysis(data) -> res

# Subgroup analysis
subgroupAnalysis(res, condition_arm2, country,
                 .which.run = "combined",
                 .tau.common = TRUE) -> sg
plot(sg, "condition_arm2")
plot(sg, "country")