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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "DRsurvCRT" in publications use:'
type: software
license: MIT
title: 'DRsurvCRT: Doubly-Robust Estimation for Survival Outcomes in Cluster-Randomized
  Trials'
version: 0.0.1
doi: 10.32614/CRAN.package.DRsurvCRT
abstract: 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" <https://doi.org/10.48550/arXiv.2510.08438>.
authors:
- family-names: Fang
  given-names: Xi
  email: x.fang@yale.edu
- family-names: Li
  given-names: Fan
  email: fan.li@example.edu
repository: https://cran.r-universe.dev
commit: aa436d0f01cfb0ec59176851622a10bb802bf323
date-released: '2025-12-30'
contact:
- family-names: Fang
  given-names: Xi
  email: x.fang@yale.edu