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  "Package": "Landmarking",
  "Title": "Analysis using Landmark Models",
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  "Authors@R": "c(\nperson(given = \"Isobel\",\nfamily = \"Barrott\",\nrole = c(\"aut\", \"cre\"),\nemail = \"isobel.barrott@gmail.com\"),\nperson(given = \"Jessica\",\nfamily = \"Barrett\",\nrole = c(\"aut\"),\nemail = \"jessica.barrett@mrc-bsu.cam.ac.uk\"),\nperson(given = \"Ruth\",\nfamily = \"Keogh\",\nrole = c(\"ctb\")),\nperson(given = \"Michael\",\nfamily = \"Sweeting\",\nrole = c(\"ctb\")),\nperson(given = \"David\",\nfamily = \"Stevens\",\nrole = c(\"ctb\")))",
  "Description": "The landmark approach allows survival predictions to be\nupdated dynamically as new measurements from an individual are\nrecorded. The idea is to set predefined time points, known as\n\"landmark times\", and form a model at each landmark time using\nonly the individuals in the risk set. This package allows the\nlongitudinal data to be modelled either using the last\nobservation carried forward or linear mixed effects modelling.\nThere is also the option to model competing risks, either\nthrough cause-specific Cox regression or Fine-Gray regression.\nTo find out more about the methods in this package, please see\n<https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.",
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  "Date/Publication": "2026-05-16 18:25:19 UTC",
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      "title": "Simulated repeat measurement and time-to-event data",
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        "id",
        "smoking",
        "diabetes",
        "ethnicity",
        "dob",
        "index",
        "sbp_stnd",
        "tchdl_stnd",
        "response_time_tchdl_stnd",
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        "find_LME_risk_set"
      ]
    },
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      "title": "Find the risk set for a landmark model (LOCF)",
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      "topics": [
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      "title": "Fit a survival sub-model as part of a landmarking analysis",
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      ]
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