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  "Title": "Sample Size Estimations for Planning Multi-Reader Multi-Case\n(MRMC) Studies Without Pilot Data",
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  "Authors@R": "c(person(c(\"Dennis\"), \"Robert\",\nemail = \"dennis.robert.nm@gmail.com\",\nrole = c(\"aut\", \"cre\")))",
  "Author": "Dennis Robert [aut, cre]",
  "Maintainer": "Dennis Robert <dennis.robert.nm@gmail.com>",
  "Description": "Sample size estimations for MRMC studies based on the\nObuchowski-Rockette (OR) methodology is implemented. The\nfunction can calculate sample sizes where the endpoint of\ninterest in the study is either ROC AUC\n(Area-Under-the-Receiver-Operating-Characteristics-Curve) or\nsensitivity. The package can also return sample sizes for\nstudies expected to have clustering effect (e.g.- multiple\npulmonary nodules per patient). All calculations assume that\nthe study design is fully crossed (paired-reader, paired-case)\nwhere each reader reads/interprets each case and that there are\ntwo interventions/imaging-modalities/techniques in the study.\nIn addition to MRMC, it can also be used to estimate sample\nsizes for standalone studies where sensitivity or AUC are the\nprimary endpoints. The methods implemented are based on the\nmethods described in Zhou et.al. (2011)\n<doi:10.1002/9780470906514> and Obuchowski (2000)\n<doi:10.2214/ajr.175.3.1750603>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "URL": "https://github.com/technOslerphile/MRMCsamplesize",
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    "description": "Physician data scientist. Experienced in clinical development of Medical AI decision support algorithms and in real-world data (RWD) Observational Studies"
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      "title": "Estimate sample sizes for MRMC studies",
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