Simulations supporting paper: 'Estimating the Variance of Covariate-Adjusted Estimators of Average Treatment Effects in Clinical Trials with Binary Endpoints', Magirr et al (2024)
This repo contains the reproducible code for simulation results in the paper by Magirr et al. (2024). Simulations are based on variance estimators as implemented in the {beeca} package.
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R/extreme_scenario.Rmd: Results for section 4 ('Extreme scenario'). -
R/realistic_scenario.Rmd: Results for section 4 ('Realistic scenario'). -
R/misspecified_scenario.Rmd: Results for section 4 ('Model misspecification'). -
R/plot_ge_vs_ye.Rmd: Results for section 5 ('Assessing conservativeness'). -
R/utils: Helper functions for simulation machinery.
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sessionInfo.yaml: snapshot of R session to aid reproducibility.
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Ge, Miaomiao, L Kathryn Durham, R Daniel Meyer, Wangang Xie, and Neal Thomas. 2011. "Covariate-Adjusted Difference in Proportions from Clinical Trials Using Logistic Regression and Weighted Risk Differences." Drug Information Journal: DIJ/Drug Information Association 45: 481--93. https://doi.org/10.1177/009286151104500409
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Magirr, Dominic, Mark Baillie, Craig Wang, and Alexander Przybylski. 2024. “Estimating the Variance of Covariate-Adjusted Estimators of Average Treatment Effects in Clinical Trials with Binary Endpoints.” OSF. May 16. https://osf.io/9mp58.
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Przybylski A, Baillie M, Wang C, Magirr D (2024). beeca: Binary Endpoint Estimation with Covariate Adjustment. R package version 0.1.2, https://openpharma.github.io/beeca/
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Ye, Ting, Marlena Bannick, Yanyao Yi, and Jun Shao. 2023. "Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes." Statistical Theory and Related Fields 7 (2): 159--63. https://doi.org/10.1080/24754269.2023.2205802