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  "Title": "Doubly-Robust Estimation for Survival Outcomes in\nCluster-Randomized Trials",
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  "Authors@R": "c(\nperson(given = \"Xi\", family = \"Fang\", role = c(\"aut\",\"cre\"),email = \"x.fang@yale.edu\"),\nperson(given = \"Fan\", family = \"Li\", role = c(\"aut\"), email = \"fan.li@example.edu\")\n)",
  "Description": "Cluster-randomized trials (CRTs) assign treatment to\ngroups rather than individuals, so valid analyses must\ndistinguish cluster-level and individual-level effects and\ndefine estimands within a potential-outcomes framework. This\npackage supports right-censored survival outcomes for both\nsingle-state (binary) and multi-state settings. For\nsingle-state outcomes, it provides estimands based on\nstage-specific survival contrasts (SPCE) and restricted mean\nsurvival time (RMST). For multi-state outcomes, it provides\nSPCE as well as a generalized win-based restricted mean\ntime-in-favor estimand (RMT-IF). The package implements doubly\nrobust estimators that accommodate covariate-dependent\ncensoring and remain consistent if either the outcome model or\nthe censoring model is correctly specified. Users can choose\nmarginal Cox or gamma-frailty Cox working models for nuisance\nestimation, and inference is supported via\nleave-one-cluster-out jackknife variance and confidence\ninterval estimation. Methods are described in Fang et al.\n(2025) \"Estimands and doubly robust estimation for\ncluster-randomized trials with survival outcomes\"\n<doi:10.48550/arXiv.2510.08438>.",
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