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

Package: EvidenceSynthesis 0.5.0

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_0.5.0.tar.gz
EvidenceSynthesis_0.5.0.tar.gz(r-4.5-noble)EvidenceSynthesis_0.5.0.tar.gz(r-4.4-noble)
EvidenceSynthesis_0.5.0.tgz(r-4.4-emscripten)EvidenceSynthesis_0.5.0.tgz(r-4.3-emscripten)
EvidenceSynthesis.pdf |EvidenceSynthesis.html✨
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

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

Datasets:

ncLikelihoods - Example profile likelihoods for negative control outcomes
ooiLikelihoods - Example profile likelihoods for a synthetic outcome of interest

openjdk

3.67 score 31 scripts 470 downloads 27 exports 82 dependencies

Last updated 2 years agofrom:4b1759c186. Checks:OK: 1 WARNING: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 27 2024
R-4.5-linux	WARNING	Nov 27 2024

Exports:approximateHierarchicalNormalPosterior approximateLikelihood approximateSimplePosterior biasCorrectionInference computeBayesianMetaAnalysis computeConfidenceInterval computeFixedEffectMetaAnalysis createSimulationSettings customFunction detectApproximationType fitBiasDistribution plotBiasCorrectionInference plotBiasDistribution plotCovariateBalances plotEmpiricalNulls plotLikelihoodFit plotMcmcTrace plotMetaAnalysisForest plotPerDbMcmcTrace plotPerDbPosterior plotPosterior plotPreparedPs preparePsPlot sequentialFitBiasDistribution simulatePopulations skewNormal supportsJava8

Dependencies:Andromeda BeastJar bit bit64 blob boot cachem cli clipr coda colorspace CompQuadForm cpp11 crayon Cyclops DBI dbplyr distributional dplyr EmpiricalCalibration fansi farver fastmap generics ggdist ggplot2 glue gridExtra gtable HDInterval hms isoband labeling lattice lifecycle lme4 magrittr MASS mathjaxr Matrix memoise meta metadat metafor mgcv minqa munsell nlme nloptr numDeriv pbapply pillar pkgconfig plogr prettyunits progress purrr quadprog R6 RColorBrewer Rcpp RcppEigen RcppParallel readr rJava rlang RSQLite 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 Nov 27 2024.

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

Code used in the video vignette

Martijn Schuemie

Rendered fromVideoVignette.Rmdusingknitr::rmarkdownon Nov 27 2024.

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

Effect estimate synthesis using non-normal likelihood approximations

Martijn J. Schuemie

Rendered fromNonNormalEffectSynthesis.Rmdusingknitr::rmarkdownon Nov 27 2024.

Last update: 2023-05-08
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
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
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
Fit Bias Distribution	fitBiasDistribution
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
Fit Bias Distribution Sequentially or in Groups	sequentialFitBiasDistribution
Simulate survival data for multiple databases	simulatePopulations
The skew normal function to approximate a log likelihood function	skewNormal
Determine if Java virtual machine supports Java	supportsJava8