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-4.5)RCTrep_1.0.0.zip(r-4.4)RCTrep_1.0.0.zip(r-4.3)
RCTrep_1.0.0.tgz(r-4.5-any)RCTrep_1.0.0.tgz(r-4.4-any)RCTrep_1.0.0.tgz(r-4.3-any)
RCTrep_1.0.0.tar.gz(r-4.5-noble)RCTrep_1.0.0.tar.gz(r-4.4-noble)
RCTrep_1.0.0.tgz(r-4.4-emscripten)RCTrep_1.0.0.tgz(r-4.3-emscripten)
RCTrep.pdf |RCTrep.html✨
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:

4.68 score 8 stars 12 scripts 207 downloads 7 exports 160 dependencies

Last updated 2 years agofrom:9b53d9ea21. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 18 2025
R-4.5-winNOTEFeb 18 2025
R-4.5-macNOTEFeb 18 2025
R-4.5-linuxNOTEFeb 18 2025
R-4.4-winNOTEFeb 18 2025
R-4.4-macNOTEFeb 18 2025
R-4.3-winNOTEFeb 18 2025
R-4.3-macNOTEFeb 18 2025

Exports:call_dashboardDGMFusionGenerateSyntheticDataRCTREPSEstimator_wrapperTEstimator_wrapper

Dependencies:abindADGofTestbackportsBARTbase64encbitopsbootbroombslibcachemcarcarDatacaretcaToolschkclasscliclockcodetoolscolorspacecommonmarkcopulacorrplotcowplotcpp11crayoncvAUCdata.tableDBIDerivdiagramdigestdoBydplyre1071fansifarverfastDummiesfastmapfontawesomeforeachFormulafsfuturefuture.applygamgbmgeexgenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegowergplotsgridExtragslgtablegtoolshardhathtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothlabelinglaterlatticelavalifecyclelistenvlme4lubridatemagrittrMASSMatchItMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamitoolsModelMetricsmodelrmunsellmvtnormnlmenloptrnnetnnlsnumDerivoptmatchparallellypbkrtestpcaPPpillarpkgconfigplyrpolynompROCprodlimprogressrpromisesproxypsplinePSweightpurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRdpackrecipesreformulasreshape2rlangrlemonROCRrootSolverpartrstatixsassscalesshapeshinyshinydashboardsourcetoolsSparseMsparsevctrsSQUAREMstablediststringistringrSuperLearnersurveysurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxtable

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

Rendered fromvignette.Rmdusingknitr::rmarkdownon Feb 18 2025.

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