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Description

Synthesizing Causal Evidence in a Distributed Research Network.

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

EvidenceSynthesis

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EvidenceSynthesis is part of HADES.

Introduction

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.

Features

  • Perform a traditional fixed-effects or random-effects meta-analysis, and create a forest plot.
  • Use non-normal approximations of the per-data-site likelihood function to avoid bias when facing small and zero counts.

Example

# 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

Technology

This an R package with some parts implemented in Java.

System requirements

Requires R and Java.

Getting Started

  1. 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.

  2. In R, use the following commands to download and install EvidenceSynthesis:

    install.packages("EvidenceSynthesis")
    

User Documentation

Documentation can be found on the package website.

PDF versions of the documentation are also available:

Support

Contributing

Read here how you can contribute to this package.

License

EvidenceSynthesis is licensed under Apache License 2.0

Development

This package is being developed in RStudio.

Development status

Beta.

Metadata

Version

0.5.0

License

Unknown

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