{ "_id": "6a1064d1acfb0bcc41ca8971", "Package": "accrual", "Type": "Package", "Title": "Bayesian Accrual Prediction", "Version": "1.4", "Date": "2023-11-23", "Authors@R": "c(person(given = \"Junhao\", family = \"Liu\", email = \"liujunhao2008@gmail.com\", role = c(\"aut\", \"cre\"), comment = \"Maintainer\"), person(given = \"Yu\", family = \"Jiang\", role = c(\"aut\"), comment = \"Original author\"), person(given = \"Cen\", family = \"Wu\", role = c(\"aut\")), person(given = \"Steve\", family = \"Simon\", role = c(\"aut\")), person(given = \"Matthew S.\", family = \"Mayo\",role=c(\"aut\")), person(given = \"Rama\", family = \"Raghavan\", role = c(\"aut\")), person(given = \"Byron J.\", family = \"Gajewski\",role=c(\"aut\")))", "Maintainer": "Junhao Liu ", "Description": "Participant recruitment for medical research is\nchallenging. Slow accrual leads to delays in research. Accrual\nmonitoring during the process of recruitment is critical.\nResearchers need reliable tools to manage the accrual rate. We\ndeveloped a Bayesian method that integrates the researcher's\nexperience with previous trials and data from the current\nstudy, providing reliable predictions on accrual rate for\nclinical studies. For more details and background on these\nmethodologies, see the publications of Byron, Stephen and Susan\n(2008) , and Yu et al. (2015)\n. In this R package, Bayesian accrual\nprediction functions are presented, which can be easily used by\nstatisticians and clinical researchers.", "License": "GPL-2", "Encoding": "UTF-8", "LazyLoad": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-08 06:40:16 UTC", "User": "root" }, "Author": "Junhao Liu [aut, cre] (Maintainer), Yu Jiang [aut] (Original\nauthor), Cen Wu [aut], Steve Simon [aut], Matthew S. Mayo\n[aut], Rama Raghavan [aut], Byron J. 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