{
  "_id": "6ab0dff53d719c1f84084019",
  "Package": "NCC",
  "Title": "Simulation and Analysis of Platform Trials with Non-Concurrent\nControls",
  "Version": "1.0",
  "Author": "Pavla Krotka [aut, cre]\n(<https://orcid.org/0000-0001-5727-4270>), Marta Bofill Roig\n[aut, ths] (<https://orcid.org/0000-0002-4400-7541>), Katharina\nHees [aut], Peter Jacko [aut], Dominic Magirr [aut], Martin\nPosch [ctb] (<https://orcid.org/0000-0001-8499-8573>)",
  "Maintainer": "Pavla Krotka <pavla.krotka@upc.edu>",
  "Authors@R": "c(person(given = \"Pavla\",\nfamily = \"Krotka\",\nrole = c(\"aut\", \"cre\"),\nemail = \"pavla.krotka@upc.edu\",\ncomment = c(ORCID = \"0000-0001-5727-4270\")),\nperson(\"Marta\", \"Bofill Roig\", email = \"marta.bofill.roig@upc.edu\", role = c(\"aut\", \"ths\"), comment = c(ORCID = \"0000-0002-4400-7541\")),\nperson(\"Katharina\", \"Hees\", email = \"Katharina.Hees@pei.de\", role = c(\"aut\")),\nperson(\"Peter\", \"Jacko\", email = \"peter@berryconsultants.net\", role = c(\"aut\")),\nperson(\"Dominic\", \"Magirr\", email = \"dominic.magirr@novartis.com\", role = c(\"aut\")),\nperson(\"Martin\", \"Posch\", email = \"martin.posch@meduniwien.ac.at\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0001-8499-8573\")))",
  "Description": "Design and analysis of flexible platform trials with\nnon-concurrent controls. Functions for data generation,\nanalysis, visualization and running simulation studies are\nprovided. The implemented analysis methods are described in:\nBofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>,\nSaville et al. (2022) <doi:10.1177/17407745221112013> and\nSchmidli et al. (2014) <doi:10.1111/biom.12242>.",
  "URL": "https://pavlakrotka.github.io/NCC/,\nhttps://github.com/pavlakrotka/NCC",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "SystemRequirements": "JAGS 4.x.y",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.2",
  "VignetteBuilder": "knitr",
  "BugReports": "https://github.com/pavlakrotka/NCC/issues",
  "Config/pak/sysreqs": "cmake libgmp3-dev make libgsl0-dev jags libicu-dev",
  "Repository": "https://pavlakrotka.r-universe.dev",
  "Date/Publication": "2025-03-10 14:29:16 UTC",
  "RemoteUrl": "https://github.com/pavlakrotka/ncc",
  "RemoteRef": "HEAD",
  "RemoteSha": "f439de7b785d13669add0ba53666c96bd4d0b95f",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-09-21 07:36:57 UTC",
    "User": "root"
  },
  "_user": "pavlakrotka",
  "_type": "src",
  "_file": "NCC_1.0.tar.gz",
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  "_filesize": 1177125,
  "_sha256": "18fc55906766889e5a63243711c657724c2cf8da9c07edea5e70a91f4ab1e88a",
  "_expires": "2026-12-30T07:42:43.000Z",
  "_created": "2026-09-21T07:36:57.000Z",
  "_published": "2026-09-21T07:42:45.153Z",
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  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/pavlakrotka/actions/runs/35573456674",
  "_status": "success",
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  "_commit": {
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    "author": "Pavla Krotka <pavla.krotka@gmail.com>",
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    "message": "clean dependencies\n",
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    "uuid": 77578027,
    "orcid": "0000-0001-5727-4270"
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      "package": "ggplot2",
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  ],
  "_owner": "pavlakrotka",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_topics": [
    "clinical-trials",
    "platform-trials",
    "simulation",
    "statistical-inference",
    "jags",
    "cpp"
  ],
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    "name": "Pavla Krotka",
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/NCC"
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  "_devurl": "https://github.com/pavlakrotka/ncc",
  "_pkgdown": "https://pavlakrotka.github.io/NCC/",
  "_searchresults": 33,
  "_rbuild": "4.6.1",
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  "_homeurl": "https://github.com/pavlakrotka/ncc",
  "_realowner": "pavlakrotka",
  "_cranurl": true,
  "_releases": [
    {
      "version": "1.0",
      "date": "2023-03-03"
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  ],
  "_exports": [
    "all_models",
    "datasim_bin",
    "datasim_bin_2",
    "datasim_cont",
    "fixmodel_bin",
    "fixmodel_cal_bin",
    "fixmodel_cal_cont",
    "fixmodel_cont",
    "fixmodel_lin_cont",
    "gam_cont",
    "get_ss_matrix",
    "inv_u_trend",
    "linear_trend",
    "MAPprior_bin",
    "MAPprior_cont",
    "MAPpriorNew_cont",
    "mixmodel_AR1_cal_cont",
    "mixmodel_AR1_cont",
    "mixmodel_cal_cont",
    "mixmodel_cont",
    "mixmodel_int_cal_cont",
    "mixmodel_int_cont",
    "piecewise_cal_cont",
    "piecewise_cont",
    "plot_trial",
    "poolmodel_bin",
    "poolmodel_cont",
    "powerprior_cont",
    "seasonal_trend",
    "sepmodel_adj_bin",
    "sepmodel_adj_cont",
    "sepmodel_bin",
    "sepmodel_cont",
    "sim_study",
    "sim_study_par",
    "splines_cal_cont",
    "splines_cont",
    "sw_trend",
    "timemachine_bin",
    "timemachine_cont"
  ],
  "_help": [
    {
      "page": "datasim_bin",
      "title": "Simulate binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points",
      "topics": [
        "datasim_bin"
      ]
    },
    {
      "page": "datasim_bin_2",
      "title": "Simulate binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points using a user-specified sample size matrix",
      "topics": [
        "datasim_bin_2"
      ]
    },
    {
      "page": "datasim_cont",
      "title": "Simulate continuous data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points",
      "topics": [
        "datasim_cont"
      ]
    },
    {
      "page": "fixmodel_bin",
      "title": "Frequentist logistic regression model analysis for binary data adjusting for periods",
      "topics": [
        "fixmodel_bin"
      ]
    },
    {
      "page": "fixmodel_cal_bin",
      "title": "Frequentist logistic regression model analysis for binary data adjusting for calendar time units",
      "topics": [
        "fixmodel_cal_bin"
      ]
    },
    {
      "page": "fixmodel_cal_cont",
      "title": "Frequentist linear regression model analysis for continuous data adjusting for calendar time units",
      "topics": [
        "fixmodel_cal_cont"
      ]
    },
    {
      "page": "fixmodel_cont",
      "title": "Frequentist linear regression model analysis for continuous data adjusting for periods",
      "topics": [
        "fixmodel_cont"
      ]
    },
    {
      "page": "fixmodel_lin_cont",
      "title": "Frequentist linear regression model analysis for continuous data with linear adjustment for time",
      "topics": [
        "fixmodel_lin_cont"
      ]
    },
    {
      "page": "gam_cont",
      "title": "Generalized additive model analysis for continuous data",
      "topics": [
        "gam_cont"
      ]
    },
    {
      "page": "get_ss_matrix",
      "title": "Sample size matrix for a platform trial with a given number of treatment arms",
      "topics": [
        "get_ss_matrix"
      ]
    },
    {
      "page": "inv_u_trend",
      "title": "Generation of an inverted-u trend",
      "topics": [
        "inv_u_trend"
      ]
    },
    {
      "page": "linear_trend",
      "title": "Generation of a linear trend that starts in a given period",
      "topics": [
        "linear_trend"
      ]
    },
    {
      "page": "MAPprior_bin",
      "title": "Analysis for binary data using the MAP Prior approach",
      "topics": [
        "MAPprior_bin"
      ]
    },
    {
      "page": "MAPprior_cont",
      "title": "Analysis for continuous data using the MAP Prior approach",
      "topics": [
        "MAPprior_cont"
      ]
    },
    {
      "page": "MAPpriorNew_cont",
      "title": "Analysis for continuous data using the MAP Prior approach",
      "topics": [
        "MAPpriorNew_cont"
      ]
    },
    {
      "page": "mixmodel_AR1_cal_cont",
      "title": "Mixed regression model analysis for continuous data adjusting for calendar time units as a random factor with AR1 correlation structure",
      "topics": [
        "mixmodel_AR1_cal_cont"
      ]
    },
    {
      "page": "mixmodel_AR1_cont",
      "title": "Mixed regression model analysis for continuous data adjusting for periods as a random factor with AR1 correlation structure",
      "topics": [
        "mixmodel_AR1_cont"
      ]
    },
    {
      "page": "mixmodel_cal_cont",
      "title": "Mixed regression model analysis for continuous data adjusting for calendar time units as a random factor",
      "topics": [
        "mixmodel_cal_cont"
      ]
    },
    {
      "page": "mixmodel_cont",
      "title": "Mixed regression model analysis for continuous data adjusting for periods as a random factor",
      "topics": [
        "mixmodel_cont"
      ]
    },
    {
      "page": "mixmodel_int_cal_cont",
      "title": "Mixed regression model analysis for continuous data using the covariates treatment and calendar time unit as fixed effects and the interaction between them as a random effect",
      "topics": [
        "mixmodel_int_cal_cont"
      ]
    },
    {
      "page": "mixmodel_int_cont",
      "title": "Mixed regression model analysis for continuous data using the covariates treatment and period as fixed effects and the interaction between them as a random effect",
      "topics": [
        "mixmodel_int_cont"
      ]
    },
    {
      "page": "piecewise_cal_cont",
      "title": "Model-based analysis for continuous data using discontinuous piecewise polynomials per calendar time unit",
      "topics": [
        "piecewise_cal_cont"
      ]
    },
    {
      "page": "piecewise_cont",
      "title": "Model-based analysis for continuous data using discontinuous piecewise polynomials per period",
      "topics": [
        "piecewise_cont"
      ]
    },
    {
      "page": "plot_trial",
      "title": "Function for visualizing the simulated trial",
      "topics": [
        "plot_trial"
      ]
    },
    {
      "page": "poolmodel_bin",
      "title": "Pooled analysis for binary data",
      "topics": [
        "poolmodel_bin"
      ]
    },
    {
      "page": "poolmodel_cont",
      "title": "Pooled analysis for continuous data",
      "topics": [
        "poolmodel_cont"
      ]
    },
    {
      "page": "powerprior_cont",
      "title": "Analysis for continuous data using the power prior approach",
      "topics": [
        "powerprior_cont"
      ]
    },
    {
      "page": "seasonal_trend",
      "title": "Generation of a seasonal trend",
      "topics": [
        "seasonal_trend"
      ]
    },
    {
      "page": "sepmodel_adj_bin",
      "title": "Separate analysis for binary data adjusted for periods",
      "topics": [
        "sepmodel_adj_bin"
      ]
    },
    {
      "page": "sepmodel_adj_cont",
      "title": "Separate analysis for continuous data adjusted for periods",
      "topics": [
        "sepmodel_adj_cont"
      ]
    },
    {
      "page": "sepmodel_bin",
      "title": "Separate analysis for binary data",
      "topics": [
        "sepmodel_bin"
      ]
    },
    {
      "page": "sepmodel_cont",
      "title": "Separate analysis for continuous data",
      "topics": [
        "sepmodel_cont"
      ]
    },
    {
      "page": "sim_study",
      "title": "Wrapper function performing simulation studies for a given set of scenarios (not parallelized)",
      "topics": [
        "sim_study"
      ]
    },
    {
      "page": "sim_study_par",
      "title": "Wrapper function performing simulation studies for a given set of scenarios (parallelized on replication level)",
      "topics": [
        "sim_study_par"
      ]
    },
    {
      "page": "splines_cal_cont",
      "title": "Spline regression analysis for continuous data with knots placed according to calendar time units",
      "topics": [
        "splines_cal_cont"
      ]
    },
    {
      "page": "splines_cont",
      "title": "Spline regression analysis for continuous data with knots placed according to periods",
      "topics": [
        "splines_cont"
      ]
    },
    {
      "page": "sw_trend",
      "title": "Generation of stepwise trend with equal jumps between periods",
      "topics": [
        "sw_trend"
      ]
    },
    {
      "page": "timemachine_bin",
      "title": "Time machine analysis for binary data",
      "topics": [
        "timemachine_bin"
      ]
    },
    {
      "page": "timemachine_cont",
      "title": "Time machine analysis for continuous data",
      "topics": [
        "timemachine_cont"
      ]
    }
  ],
  "_readme": "https://github.com/pavlakrotka/ncc/raw/HEAD/README.md",
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        "1) Installing JAGS",
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      "headings": [
        "Non-concurrent controls in platform trials"
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