Package: beeca 0.2.0

Alex Przybylski

beeca: Binary Endpoint Estimation with Covariate Adjustment

Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Authors:Alex Przybylski [cre, aut], Mark Baillie [aut], Craig Wang [aut], Dominic Magirr [aut]

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manual.pdf |manual.html✨
card.svg |card.png
beeca/json (API)
NEWS

# Install 'beeca' in R:

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

Bug tracker:https://github.com/openpharma/beeca/issues

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

Datasets:

ge_macro_trial01 - Output from the Ge et al (2011) SAS macro applied to the trial01 dataset
margins_trial01 - Output from the Margins SAS macro applied to the trial01 dataset
trial01 - Example trial dataset 01
trial02_cdisc - Example CDISC Clinical Trial Dataset in ADaM Format

On CRAN:

ard covariate-adjustment delta-method marginal-effects marginal-estimand

5.36 score 7 stars 1 packages 11 scripts 645 downloads 6 exports 18 dependencies

Last updated from:4b72c14afc. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	153
source / vignettes	OK	205
linux-release-x86_64	OK	142
macos-release-arm64	OK	93
macos-oldrel-arm64	OK	82
windows-devel	OK	106
windows-release	OK	117
windows-oldrel	OK	111
wasm-release	OK	128

Exports:apply_contrast average_predictions estimate_varcov get_marginal_effect predict_counterfactuals sanitize_model

Dependencies:cli dplyr generics glue lattice lifecycle magrittr pillar pkgconfig R6 rlang sandwich tibble tidyselect utf8 vctrs withr zoo

Introduction to estimating a marginal estimand with beeca

Rendered fromestimand_and_implementations.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2024-11-12
Started: 2024-06-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Apply contrast to calculate marginal estimate of treatment effect and corresponding standard error	apply_contrast
Average over counterfactual predictions	average_predictions
Estimate variance-covariance matrix for marginal estimand based on GLM model	estimate_varcov
Output from the Ge et al (2011) SAS macro applied to the trial01 dataset	ge_macro_trial01
Estimate marginal treatment effects using a GLM working model	get_marginal_effect
Output from the Margins SAS macro applied to the trial01 dataset	margins_trial01
Predict counterfactual outcomes in GLM models	predict_counterfactuals
Example trial dataset 01	trial01
Example CDISC Clinical Trial Dataset in ADaM Format	trial02_cdisc