{ "_id": "6ac4c41bf511ae58c09a10b0", "Package": "BayesianPlatformDesignTimeTrend", "Title": "Simulate and Analyse Bayesian Platform Trial with Time Trend", "Version": "1.2.0", "Author": "Ziyan Wang [aut, cre], David Woods [ctb]", "Maintainer": "Ziyan Wang ", "Authors@R": "c(person(given = \"Ziyan\",\nfamily = \"Wang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"zw7g20@soton.ac.uk\"),\nperson(given = \"David\",\nfamily = \"Woods\",\nrole = c(\"ctb\"),\nemail = \"D.Woods@southampton.ac.uk\"))", "Description": "Simulating the sequential multi-arm multi-stage or\nplatform trial with Bayesian approach using the 'rstan'\npackage, which provides the R interface for the Stan. This\npackage supports fixed ratio and Bayesian adaptive\nrandomization approaches for randomization. Additionally, it\nallows for the study of time trend problems in platform trials.\nThere are demos available for a multi-arm multi-stage trial\nwith two different null scenarios, as well as for Bayesian\ntrial cutoff screening. The Bayesian adaptive randomisation\napproaches are described in: Trippa et al. (2012)\n and Wathen et al. (2017)\n. The randomisation algorithm is\ndescribed in: Zhao W . The\nanalysis methods of time trend effect in platform trial are\ndescribed in: Saville et al. (2022)\n and Bofill Roig et al. 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