Package: beeca 0.1.3.9000

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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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

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:

6 exports 4 stars 1.50 score 19 dependencies 5 scripts 840 downloads

Last updated 3 months agofrom:6c269af552. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 03 2024
R-4.5-win	OK	Sep 03 2024
R-4.5-linux	OK	Sep 03 2024
R-4.4-win	OK	Sep 03 2024
R-4.4-mac	OK	Sep 03 2024
R-4.3-win	OK	Sep 03 2024
R-4.3-mac	OK	Sep 03 2024

Exports:apply_contrast average_predictions estimate_varcov get_marginal_effect predict_counterfactuals sanitize_model

Dependencies:cli dplyr fansi 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 Sep 03 2024.

Last update: 2024-06-14
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