EvidenceSynthesis is part of HADES.
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:
<- simulatePopulations()
populations
# Fit a Cox regression at each data site, and approximate likelihood function:
<- function(population) {
fitModelInDatabase <- Cyclops::createCyclopsData(Surv(time, y) ~ x + strata(stratumId),
cyclopsData data = population,
modelType = "cox")
<- Cyclops::fitCyclopsModel(cyclopsData)
cyclopsFit <- approximateLikelihood(cyclopsFit, parameter = "x", approximation = "custom")
approximation return(approximation)
}<- lapply(populations, fitModelInDatabase)
approximations <- do.call("rbind", approximations)
approximations
# At study coordinating center, perform meta-analysis using per-site approximations:
<- computeBayesianMetaAnalysis(approximations)
estimate
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
This an R package with some parts implemented in Java.
Requires R and Java.
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.
EvidenceSynthesis is licensed under Apache License 2.0
This package is being developed in RStudio.
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