This R package contains routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study. This includes functions for performing meta-analysis and forest plots.
# Simulate some data for this example:
populations <- simulatePopulations()
# Fit a Cox regression at each data site, and approximate likelihood function:
fitModelInDatabase <- function(population) {
cyclopsData <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
data = population,
modelType = "cox")
cyclopsFit <- Cyclops::fitCyclopsModel(cyclopsData)
approximation <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
return(approximation)
}
approximations <- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)
# At study coordinating center, perform meta-analysis using per-site approximations:
estimate <- computeBayesianMetaAnalysis(approximations)
estimate
# mu mu95Lb mu95Ub muSe tau tau95Lb tau95Ub logRr seLogRr
# 1 0.5770562 -0.2451619 1.382396 0.4154986 0.2733942 0.004919128 0.7913512 0.5770562 0.4152011
Make sure your R environment is properly configured. This means that Java must be installed. See these instructions for how to configure your R environment.
In R, use the following commands to download and install EvidenceSynthesis:
install.packages("EvidenceSynthesis")
Documentation can be found on the package website.
PDF versions of the documentation are also available:
Read here how you can contribute to this package.