{ "_id": "6ac68610ca1b382b34ef4155", "Package": "blendR", "Title": "Blended Survival Curves", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Nathan\", \"Green\", , \"n.green@ucl.ac.uk\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-2745-1736\", ROR = \"02jx3x895\")),\nperson(\"Zhaojing\", \"Che\", , \"blendr-pkg@proton.me\", role = c(\"aut\", \"cph\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2245-1606\", ROR = \"052gg0110\"))\n)", "Description": "Create a blended curve from two survival curves, which is\nparticularly useful for survival extrapolation in health\ntechnology assessment. The main idea is to mix a flexible model\nthat fits the observed data well with a parametric model that\nencodes assumptions about long-term survival. The two curves\nare blended into a single survival curve that is identical to\nthe first model over the range of observed times and gradually\napproaches the parametric model over the extrapolation period\nbased on a given weight function. This approach allows for the\ninclusion of external information, such as data from registries\nor expert opinion, to guide long-term extrapolations,\nespecially when dealing with immature trial data. See Che et\nal. 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