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  "Title": "Survival Analysis for AdVerse Events with VarYing Follow-Up\nTimes",
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  "Date": "2025-04-15",
  "Authors@R": "c(\nperson(\"Thomas\", \"Kuenzel\", email = \"thomas.kuenzel@roche.com\", role = c(\"aut\", \"cre\")),\nperson(\"Kaspar\", \"Rufibach\", email = \"kaspar.rufibach@roche.com\", role = \"aut\"),\nperson(\"Regina\", \"Stegherr\", email = \"regina.stegherr@uni-ulm.de\", role = \"aut\"),\nperson(\"Daniel\", \"Sabanés Bové\", email = \"daniel@rconis.com\", role = \"aut\"),\nperson(\"F. Hoffmann-La Roche AG\", role = c(\"cph\", \"fnd\")),\nperson(\"Universität Ulm\", role = c(\"cph\", \"fnd\"))\n)",
  "Description": "The SAVVY (Survival Analysis for AdVerse Events with\nVarYing Follow-Up Times) project is a consortium of academic\nand pharmaceutical industry partners that aims to improve the\nanalyses of adverse event (AE) data in clinical trials through\nthe use of survival techniques appropriately dealing with\nvarying follow-up times and competing events, see Stegherr,\nSchmoor, Beyersmann, et al. (2021)\n<doi:10.1186/s13063-021-05354-x>. Although statistical\nmethodologies have advanced, in AE analyses often the incidence\nproportion, the incidence density or a non-parametric\nKaplan-Meier estimator are used, which either ignore censoring\nor competing events. This package contains functions to easily\nconduct the proposed improved AE analyses.",
  "License": "Apache License 2.0",
  "URL": "https://openpharma.github.io/savvyr/",
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