{ "_id": "6a453c166ff6f23575527cd5", "Package": "BayesPET", "Title": "Bayesian Prediction of Event Times for Blinded Randomized\nControlled Trials", "Version": "0.1.0", "Date": "2026-02-04", "Authors@R": "c(\nperson(\"Xinyi\", \"He\", , \"xinyi.he@uth.tmc.edu\", role = c(\"cre\", \"aut\")),\nperson(\"Jingyan\", \"Fu\", , role = \"aut\"),\nperson(\"Ying\", \"Yuan\", , role = c(\"aut\", \"cph\"))\n)", "Description": "Bayesian methods for predicting the calendar time at which\na target number of events is reached in clinical trials. The\nmethodology applies to both blinded and unblinded settings and\njointly models enrollment, event-time, and censoring processes.\nThe package provides tools for trial data simulation, model\nfitting using 'Stan' via the 'rstan' interface, and event time\nprediction under a wide range of trial designs, including\nvarying sample sizes, enrollment patterns, treatment effects,\nand event or censoring time distributions. The package is\nintended to support interim monitoring, operational planning,\nand decision-making in clinical trial development. Methods are\ndescribed in Fu et al. 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