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Run subgroup analyses

Source: R/subgroupAnalysis.R
subgroupAnalysis.Rd

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

Usage

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

Arguments

.model

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.

.which.run

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.

.round.digits

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

.nnt.cer

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.

.tau.common

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.

.html

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

Value

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.

Details

For more details see the Get Started vignette.

See also

runMetaAnalysis

Author

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

Examples

if (FALSE) {
data("depressionPsyCtr")

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")
}