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DRsurvCRT  

Doubly-Robust Estimation for Survival Outcomes in Cluster-Randomized Trials
View on CRAN: Click here


Download and install DRsurvCRT package within the R console
Install from CRAN:
install.packages("DRsurvCRT")

Install from Github:
library("remotes")
install_github("cran/DRsurvCRT")

Install by package version:
library("remotes")
install_version("DRsurvCRT", "0.0.1")



Attach the package and use:
library("DRsurvCRT")
Maintained by
Xi Fang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-12-30
Latest Update: 2025-12-30
Description:
Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>.
How to cite:
Xi Fang (2025). DRsurvCRT: Doubly-Robust Estimation for Survival Outcomes in Cluster-Randomized Trials. R package version 0.0.1, https://cran.r-project.org/web/packages/DRsurvCRT. Accessed 24 Oct. 2026.
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