This package is considered a duplicate. The official version of this package is found at:https://ohdsi.r-universe.dev/EvidenceSynthesis

Package: EvidenceSynthesis 1.1.0

Martijn Schuemie

EvidenceSynthesis: 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.

Authors:Martijn Schuemie [aut, cre], Marc A. Suchard [aut], Fan Bu [aut], Observational Health Data Science and Informatics [cph]

EvidenceSynthesis_1.1.0.tar.gz
EvidenceSynthesis_1.1.0.tar.gz(r-4.7-any)EvidenceSynthesis_1.1.0.tar.gz(r-4.6-any)
EvidenceSynthesis_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EvidenceSynthesis/json (API)
NEWS

# Install 'EvidenceSynthesis' in R:

install.packages('EvidenceSynthesis', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ohdsi/evidencesynthesis/issues

Pkgdown/docs site:https://ohdsi.github.io

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

Datasets:

hmaLikelihoodList - Example profile likelihoods for hierarchical meta analysis with bias correction
likelihoodProfileLists - A bigger example of profile likelihoods for hierarchical meta analysis with bias correction
ncLikelihoods - Example profile likelihoods for negative control outcomes
ooiLikelihoods - Example profile likelihoods for a synthetic outcome of interest

On CRAN:

openjdk

4.12 score 44 scripts 409 downloads 39 exports 80 dependencies

Last updated from:8cce8c4458. Checks:2 WARNING, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	WARNING	281
source / vignettes	OK	282
linux-release-x86_64	WARNING	295
wasm-release	OK	149

Exports:approximateHierarchicalNormalPosterior approximateLikelihood approximateSimplePosterior biasCorrectionInference buildLabelReferences computeBayesianMetaAnalysis computeConfidenceInterval computeFixedEffectMetaAnalysis computeHierarchicalMetaAnalysis constructDataModel createApproximations createSccsSimulationSettings createSimulationSettings customFunction detectApproximationType extractSourceSpecificEffects fitBiasDistribution generateBayesianHMAsettings hermiteInterpolation loadCyclopsLibraryForJava plotBiasCorrectionInference plotBiasDistribution plotCovariateBalances plotEmpiricalNulls plotLikelihoodFit plotMcmcTrace plotMetaAnalysisForest plotPerDbMcmcTrace plotPerDbPosterior plotPosterior plotPreparedPs preparePsPlot prepareSccsIntervalData sequentialFitBiasDistribution simulateMetaAnalysisWithNegativeControls simulatePopulations skewNormal summarizeChain supportsJava8

Dependencies:Andromeda BeastJar bit bit64 blob boot cli clipr coda CompQuadForm cpp11 crayon Cyclops DBI dbplyr digest distributional dplyr duckdb EmpiricalCalibration farver generics ggdist ggplot2 glue gridExtra gtable HDInterval hms isoband labeling lattice lifecycle lme4 magrittr MASS mathjaxr Matrix memuse meta metabook metadat metafor minqa nlme nloptr numDeriv pbapply pillar pkgconfig prettyunits progress purrr quadprog R6 rbibutils RColorBrewer Rcpp RcppEigen Rdpack readr reformulas rJava rlang S7 scales stringi stringr survival tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom withr xml2 zip

Bayesian adaptive bias correction using profile likelihoods

Fan Bu

Rendered fromBayesianBiasCorrection.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-08-26
Started: 2023-05-08

Code used in the video vignette

Martijn Schuemie

Rendered fromVideoVignette.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-08-26
Started: 2023-04-05

Effect estimate synthesis using non-normal likelihood approximations

Martijn J. Schuemie

Rendered fromNonNormalEffectSynthesis.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-08-26
Started: 2020-11-19

Citation

Readme and manuals

Help Manual

Help page	Topics
Approximate Bayesian posterior for hierarchical Normal model	approximateHierarchicalNormalPosterior
Approximate a likelihood function	approximateLikelihood
Approximate simple Bayesian posterior	approximateSimplePosterior
Bias Correction with Inference	biasCorrectionInference
Build a list of references that map likelihood names to integer labels for later use	buildLabelReferences
Compute a Bayesian random-effects meta-analysis	computeBayesianMetaAnalysis
Compute the point estimate and confidence interval given a likelihood function approximation	computeConfidenceInterval
Compute a fixed-effect meta-analysis	computeFixedEffectMetaAnalysis
Compute a Bayesian random-effects hierarchical meta-analysis	computeHierarchicalMetaAnalysis
Construct 'DataModel' objects from approximate likelihood or profile likelihood data	constructDataModel
Create likelihood approximations from individual-trajectory data	createApproximations
Create SCCS simulation settings	createSccsSimulationSettings
Create simulation settings	createSimulationSettings
A custom function to approximate a log likelihood function	customFunction
Detect the type of likelihood approximation based on the data format	detectApproximationType
Compute source-specific biases and bias-corrected estimates from hierarchical meta analysis results	extractSourceSpecificEffects
Fit Bias Distribution	fitBiasDistribution
Generate settings for the Bayesian random-effects hierarchical meta-analysis model	generateBayesianHMAsettings
Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function	hermiteInterpolation
Example profile likelihoods for hierarchical meta analysis with bias correction	hmaLikelihoodList
A bigger example of profile likelihoods for hierarchical meta analysis with bias correction	likelihoodProfileLists
Load the Cyclops dynamic C++ library for use in Java	loadCyclopsLibraryForJava
Example profile likelihoods for negative control outcomes	ncLikelihoods
Example profile likelihoods for a synthetic outcome of interest	ooiLikelihoods
Plot bias correction inference	plotBiasCorrectionInference
Plot bias distributions	plotBiasDistribution
Plot covariate balances	plotCovariateBalances
Plot empirical null distributions	plotEmpiricalNulls
Plot the likelihood approximation	plotLikelihoodFit
Plot MCMC trace	plotMcmcTrace
Create a forest plot	plotMetaAnalysisForest
Plot MCMC trace for individual databases	plotPerDbMcmcTrace
Plot posterior density per database	plotPerDbPosterior
Plot posterior density	plotPosterior
Plot the propensity score distribution	plotPreparedPs
Prepare to plot the propensity score distribution	preparePsPlot
Prepare SCCS interval data for pooled analysis	prepareSccsIntervalData
Fit Bias Distribution Sequentially or in Groups	sequentialFitBiasDistribution
Simulate survival data across a federated data network, with negative control outcomes as well.	simulateMetaAnalysisWithNegativeControls
Simulate survival data for multiple databases	simulatePopulations
The skew normal function to approximate a log likelihood function	skewNormal
Utility function to summarize MCMC samples (posterior mean, median, HDI, std, etc.)	summarizeChain
Determine if Java virtual machine supports Java	supportsJava8