Package: RCTrep 1.0.0

RCTrep: Validation of Estimates of Treatment Effects in Observational Data

Validates estimates of (conditional) average treatment effects obtained using observational data by a) making it easy to obtain and visualize estimates derived using a large variety of methods (G-computation, inverse propensity score weighting, etc.), and b) ensuring that estimates are easily compared to a gold standard (i.e., estimates derived from randomized controlled trials). 'RCTrep' offers a generic protocol for treatment effect validation based on four simple steps, namely, set-selection, estimation, diagnosis, and validation. 'RCTrep' provides a simple dashboard to review the obtained results. The validation approach is introduced by Shen, L., Geleijnse, G. and Kaptein, M. (2023) <doi:10.21203/rs.3.rs-2559287/v1>.

Authors:Lingjie Shen [aut, cre, cph], Gijs Geleijnse [aut], Maurits Kaptein [aut]

RCTrep_1.0.0.tar.gz
RCTrep_1.0.0.zip(r-4.7)RCTrep_1.0.0.zip(r-4.6)RCTrep_1.0.0.zip(r-4.5)
RCTrep_1.0.0.tgz(r-4.6-any)RCTrep_1.0.0.tgz(r-4.5-any)
RCTrep_1.0.0.tar.gz(r-4.7-any)RCTrep_1.0.0.tar.gz(r-4.6-any)
RCTrep_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RCTrep/json (API)
NEWS

# Install 'RCTrep' in R:
install.packages('RCTrep', repos = c('https://duolajiang.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/duolajiang/rctrep/issues

Datasets:

On CRAN:

Conda:

4.68 score 8 stars 12 scripts 232 downloads 7 exports 165 dependencies

Last updated from:9b53d9ea21. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE228
source / vignettesOK270
linux-release-x86_64NOTE231
macos-release-arm64NOTE197
macos-oldrel-arm64NOTE242
windows-develNOTE165
windows-releaseNOTE147
windows-oldrelNOTE159
wasm-releaseOK162

Exports:call_dashboardDGMFusionGenerateSyntheticDataRCTREPSEstimator_wrapperTEstimator_wrapper

Dependencies:abindADGofTestbackportsBARTbase64encbitopsbootbroombslibcachemcarcarDatacaretcaToolschkclasscliclockclustercodetoolscolorspacecommonmarkcopulacorrplotcowplotcpp11cvAUCdata.tableDBIDerivdiagramdigestdoBydplyre1071farverfastDummiesfastmapfontawesomeforeachforecastFormulafracdifffsfuturefuture.applygamgbmgeexgenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegowergplotsgridExtragslgtablegtoolshardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lmtestlubridatemagrittrMASSMatchItMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamitoolsModelMetricsmodelrmvtnormnlmenloptrnnetnnlsnumDerivoptmatchotelparallellypbkrtestpcaPPpillarpkgconfigplyrpolynompROCprodlimprogressrpromisesproxypsplinePSweightpurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRdpackrecipesreformulasreshape2rlangrlemonROCRrootSolverpartrstatixS7sassscalesshapeshinyshinydashboardsourcetoolsSparseMsparsevctrsSQUAREMstablediststringistringrSuperLearnersurveysurvivaltibbletidyrtidyselecttimechangetimeDatetzdburcautf8vctrsviridisLitewithrxtablezoo

RCTrep: An R package for replicating treatment effect estimates of a randomized control trial using observational data: A Vignette

Rendered fromvignette.Rmdusingknitr::rmarkdownon May 07 2026.

Last update: 2023-03-28
Started: 2021-12-16

Readme and manuals

Help Manual

Help pageTopics
Visualizing validation results according to four steps, namely, set-selection, estimation, diagnosis, and validationcall_dashboard
Generating RCT data or observational data for the examples used in the packageDGM
Validation of estimates of conditional average treatment effects in objects of class 'TEstimator' and 'SEstimator'.Fusion
Generating the synthetic RCT data given marginal distribution of each covariateGenerateSyntheticData
Aggregated data derived from paper of QUASAR trialquasar.agg
An object of class TEstimator_Synthetic using quasar.syntheticquasar.obj
A synthetic QUASAR trial dataset, where outcome is a binary variable, treatment is a binary variable.quasar.synthetic
Replicate treatment effect estimates obtained from a randomized control trial using observational dataRCTREP
Estimating the weighted conditional average treatment effects in 'source.obj' based on input objects 'source.obj' and 'target.obj' of class 'TEstimator'.SEstimator_wrapper
A dataset of simulated observational data, where outcome is binary variable. The data is filtered after compared to target.binary.datasource.binary.data
A data set of simulated observational data, where outcome is continuous variable, treatment is a binary variable.source.data
A dataset of simulated RCT data, where outcome is binary variable. The data is filtered after compared to source.binary.datatarget.binary.data
A data set of simulated RCT data, where outcome is continuous variable, treatment is a binary variable.target.data
Estimating conditional average treatment effectsTEstimator_wrapper