Package: RCTCovAdj 0.1.0

RCTCovAdj: Covariate Adjustment for Randomized Controlled Trials

Implements covariate-adjusted estimation and inference for marginal mean differences in two-arm randomized controlled trials with continuous outcomes. Methods include the unadjusted difference in means, common-slope analysis of covariance, interacted standardization with joint influence-function inference, and cross-fitted augmentation. The package also provides analytic power and sample-size calculations, reproducible simulation designs, and article replication workflows, including tools for verifying and analyzing an authorized local trial-data file. The adjustment methods build on Tsiatis et al. (2008) <doi:10.1002/sim.3113> and Lin (2013) <doi:10.1214/12-AOAS583>.

Authors:Se Yoon Lee [aut, cre, cph]

RCTCovAdj_0.1.0.tar.gz
RCTCovAdj_0.1.0.zip(r-4.7-any)RCTCovAdj_0.1.0.zip(r-4.6-any)RCTCovAdj_0.1.0.zip(r-4.5-any)
RCTCovAdj_0.1.0.tgz(r-4.6-any)RCTCovAdj_0.1.0.tgz(r-4.5-any)
RCTCovAdj_0.1.0.tar.gz(r-4.7-any)RCTCovAdj_0.1.0.tar.gz(r-4.6-any)
RCTCovAdj_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RCTCovAdj/json (API)

# Install 'RCTCovAdj' in R:
install.packages('RCTCovAdj', repos = c('https://yain22.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 15 exports 1 dependencies

Last updated from:996c8b4eac. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK149
source / vignettesOK196
linux-releaseOK129
macos-releaseOK110
macos-oldrelOK80
windows-develOK120
windows-releaseOK75
windows-oldrelOK87
wasm-releaseOK106

Exports:analyze_licoricepaper_power_designplot_licorice_resultsplot_power_designplot_simulation_resultsprepare_licorice_datarct_adjustrct_crossfitrct_powerrct_sample_sizerct_variance_factorsread_licorice_datareproduce_paper_simulationsrun_paper_simulationssimulate_rct_case

Dependencies:digest

Covariate Adjustment in Randomized Controlled Trials
Aim and statistical setting | Where the precision gain comes from | Four analysis strategies | A small simulated RCT | Standard errors after empirical standardization | Application 1: postoperative throat-pain scores | Application 2: power and sample size | Practical analysis checklist
Rendered fromcovariate-adjustment.Rmdusingknitr::rmarkdown

Last update: 2026-09-24
Started: 2026-09-24

Reproducing the Paper's Numerical Results
Reproducibility map | Simulation laws | A short executable check | Full Monte Carlo experiment | Postoperative throat-pain analysis | Power and sample-size application | Standalone replication scripts | Recommended audit sequence | References
Rendered fromreproducing-paper.Rmdusingknitr::rmarkdown

Last update: 2026-09-24
Started: 2026-09-24

Readme and manuals

Help Manual

Help pageTopics
RCTCovAdj: Covariate Adjustment for Randomized Controlled TrialsRCTCovAdj-package RCTCovAdj
Analyze the Licorice-Gargle Trial Applicationanalyze_licorice
Population Benchmarks for the Simulation Casespaper_population_benchmarks
Reproduce the Paper's Power and Sample-Size Applicationpaper_power_design
Power and Sample-Size Results from the Articlepaper_power_design_results
Monte Carlo Results from the Articlepaper_simulation_results
Plot Aggregate Results from the Licorice Trial Applicationplot_licorice_results
Plot the Paper's Power-Design Applicationplot_power_design
Plot the Paper's Simulation Resultsplot_simulation_results
Prepare the Licorice-Gargle Trial Recordsprepare_licorice_data
Print a Licorice-Application Resultprint.licorice_analysis
Print a Covariate-Adjustment Resultprint.rct_adjustment
Print a Power-Design Resultprint.rct_power_design
Print a Paper Simulation Resultprint.rct_simulation
Estimate a Marginal Mean Difference with Covariate Adjustmentrct_adjust
Methods for Covariate-Adjustment Resultsas.data.frame.rct_adjustment coef.rct_adjustment confint.rct_adjustment rct_adjustment_methods summary.rct_adjustment vcov.rct_adjustment
Cross-Fitted Covariate Adjustmentrct_crossfit
Normal-Approximation Power for a Marginal Mean Differencerct_power
Required Sample Size for a Marginal Mean Differencerct_sample_size
Analytic Variance Factors for the Paper's Gaussian Designsrct_variance_factors
Read an Authorized Licorice-Gargle Data Fileread_licorice_data
Write a Reproducible Simulation Runreproduce_paper_simulations
Run the Paper's Monte Carlo Experimentrun_paper_simulations
Simulate One Continuous-Outcome Randomized Controlled Trialsimulate_rct_case
Simulation Cases Used in the Articlesimulation_cases