{
  "_id": "6a102ec5acfb0bcc41c94734",
  "Type": "Package",
  "Package": "hce",
  "Title": "Design and Analysis of Hierarchical Composite Endpoints",
  "Version": "0.9.3",
  "Authors@R": "person(\"Samvel B.\", \"Gasparyan\", , \"gasparyan.co@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4797-2208\"))",
  "Description": "Simulate and analyze hierarchical composite endpoints.\nIncludes implementation for the kidney hierarchical composite\nendpoint as defined in Heerspink HL et al (2023) “Development\nand validation of a new hierarchical composite end point for\nclinical trials of kidney disease progression” (Journal of the\nAmerican Society of Nephrology 34 (2): 2025–2038,\n<doi:10.1681/ASN.0000000000000243>). Win odds, also called\nWilcoxon-Mann-Whitney or success odds, is the main analysis\nmethod. Other win statistics (win probability, win ratio, net\nbenefit) are also implemented in the univariate case, provided\nthere is no censoring. The win probability analysis is based on\nthe Brunner-Munzel test and uses the\nDeLong-DeLong-Clarke-Pearson variance estimator, as described\nby Brunner and Konietschke (2025) in “An unbiased rank-based\nestimator of the Mann–Whitney variance including the case of\nties” (Statistical Papers 66 (1): 20,\n<doi:10.1007/s00362-024-01635-0>). Includes implementation of a\nnew Wilson-type, compatible confidence interval for the win\nodds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in\n“A new approach to the nonparametric Behrens–Fisher problem\nwith compatible confidence intervals.” (Biometrical Journal 67\n(6), <doi:10.1002/bimj.70096>). Stratification and covariate\nadjustment are performed based on the methodology presented by\nKoch GG et al. in “Issues for covariance analysis of\ndichotomous and ordered categorical data from randomized\nclinical trials and non-parametric strategies for addressing\nthem” (Statistics in Medicine 17 (15-16): 1863–92). For a\nreview, see Gasparyan SB et al (2021) “Adjusted win ratio with\nstratification: Calculation methods and interpretation”\n(Statistical Methods in Medical Research 30 (2): 580–611,\n<doi:10.1177/0962280220942558>).",
  "License": "MIT + file LICENSE",
  "BugReports": "https://github.com/Samve/hce/issues",
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  "Packaged": {
    "Date": "2026-05-12 19:57:02 UTC",
    "User": "root"
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  "Author": "Samvel B. Gasparyan [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-4797-2208>)",
  "Maintainer": "Samvel B. Gasparyan <gasparyan.co@gmail.com>",
  "Repository": "https://samve.r-universe.dev",
  "Date/Publication": "2026-05-12 19:42:38 UTC",
  "RemoteUrl": "https://github.com/samve/hce",
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  "_created": "2026-05-12T19:57:02.000Z",
  "_published": "2026-05-22T10:24:05.733Z",
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    "id": "67ad36ca18dddb7a957c11d60e0fa83bf9d9eb47",
    "author": "Samvel B. Gasparyan <Samve@users.noreply.github.com>",
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    "message": "Merge pull request #71 from Samve/20260512\n\nrelease/version 0.9.3",
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    "name": "Samvel B. Gasparyan",
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      "package": "base",
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    },
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      "package": "knitr",
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    "name": "Samvel B. Gasparyan",
    "description": "Clinical Trialist Statistician\r\nhttps://orcid.org/0000-0002-4797-2208"
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  "_assets": [
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    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "extra/readme.md",
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  "_realowner": "samve",
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  "_releases": [
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      "date": "2022-09-26"
    },
    {
      "version": "0.0.8",
      "date": "2022-11-16"
    },
    {
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      "date": "2023-01-05"
    },
    {
      "version": "0.5.0",
      "date": "2023-01-16"
    },
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      "date": "2023-08-17"
    },
    {
      "version": "0.5.9",
      "date": "2023-10-31"
    },
    {
      "version": "0.6.0",
      "date": "2024-03-12"
    },
    {
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      "date": "2024-08-19"
    },
    {
      "version": "0.6.5",
      "date": "2024-10-16"
    },
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      "date": "2025-01-07"
    },
    {
      "version": "0.7.0",
      "date": "2025-03-05"
    },
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      "date": "2025-05-14"
    },
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      "version": "0.8.0",
      "date": "2025-07-11"
    },
    {
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      "date": "2025-08-22"
    },
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      "date": "2025-12-11"
    },
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      "date": "2026-01-29"
    },
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    }
  ],
  "_exports": [
    "as_hce",
    "calcWINS",
    "calcWO",
    "deltaWO",
    "dGLL",
    "hce",
    "hGLL",
    "HGLL",
    "IWP",
    "minWO",
    "pGLL",
    "powerWO",
    "propWINS",
    "qGLL",
    "regWO",
    "rGLL",
    "rweibullGF",
    "simHCE",
    "simKHCE",
    "simORD",
    "simTTE",
    "sizeWO",
    "sizeWR",
    "stratWO",
    "summaryWO"
  ],
  "_datasets": [
    {
      "name": "ADET",
      "title": "Event-Time dataset for kidney outcomes.",
      "object": "ADET",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "AVAL",
        "PARAM",
        "PARAMCD",
        "PARAMN",
        "TRTPN"
      ],
      "rows": 604,
      "table": true,
      "tojson": true
    },
    {
      "name": "ADLB",
      "title": "Laboratory dataset for Glomerular Filtration Rate (GFR) measurements.",
      "object": "ADLB",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTPN",
        "AVAL",
        "ADAY",
        "AVISITN",
        "PARAM",
        "PARAMCD",
        "PARAMN"
      ],
      "rows": 13980,
      "table": true,
      "tojson": true
    },
    {
      "name": "ADSL",
      "title": "Baseline characteristics dataset of patients with kidney function assessments.",
      "object": "ADSL",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTPN",
        "EGFRBL",
        "STRATAN"
      ],
      "rows": 1500,
      "table": true,
      "tojson": true
    },
    {
      "name": "COVID19",
      "title": "COVID-19 ordinal scale dataset (full report).",
      "object": "COVID19",
      "class": [
        "data.frame"
      ],
      "fields": [
        "TRTP",
        "GROUP"
      ],
      "rows": 1062,
      "table": true,
      "tojson": true
    },
    {
      "name": "COVID19b",
      "title": "COVID-19 ordinal scale dataset (preliminary report).",
      "object": "COVID19b",
      "class": [
        "data.frame"
      ],
      "fields": [
        "TRTP",
        "GROUP"
      ],
      "rows": 844,
      "table": true,
      "tojson": true
    },
    {
      "name": "COVID19plus",
      "title": "COVID-19 ordinal scale dataset for a combination therapy.",
      "object": "COVID19plus",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTP",
        "GROUP",
        "BASE"
      ],
      "rows": 1033,
      "table": true,
      "tojson": true
    },
    {
      "name": "HCE1",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "object": "HCE1",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTP",
        "GROUP",
        "GROUPN",
        "AVALT",
        "AVAL0",
        "AVAL",
        "PADY"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "HCE2",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "object": "HCE2",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTP",
        "GROUP",
        "GROUPN",
        "AVALT",
        "AVAL0",
        "AVAL",
        "PADY"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "HCE3",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "object": "HCE3",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTP",
        "GROUP",
        "GROUPN",
        "AVALT",
        "AVAL0",
        "AVAL",
        "PADY"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "HCE4",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "object": "HCE4",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "TRTP",
        "GROUP",
        "GROUPN",
        "AVALT",
        "AVAL0",
        "AVAL",
        "PADY"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "KHCE",
      "title": "Kidney Hierarchical Composite Endpoint dataset.",
      "object": "KHCE",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ID",
        "AVAL0",
        "GROUP",
        "PARAMCD",
        "PARAMN",
        "GROUPN",
        "AVAL",
        "TRTPN",
        "EGFRBL",
        "STRATAN",
        "TRTP",
        "PADY"
      ],
      "rows": 1500,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "ADET",
      "title": "Event-Time dataset for kidney outcomes.",
      "topics": [
        "ADET"
      ]
    },
    {
      "page": "ADLB",
      "title": "Laboratory dataset for Glomerular Filtration Rate (GFR) measurements.",
      "topics": [
        "ADLB"
      ]
    },
    {
      "page": "ADSL",
      "title": "Baseline characteristics dataset of patients with kidney function assessments.",
      "topics": [
        "ADSL"
      ]
    },
    {
      "page": "as_hce",
      "title": "A generic function for coercing data structures to 'hce' objects",
      "topics": [
        "as_hce"
      ]
    },
    {
      "page": "as_hce.data.frame",
      "title": "Coerce a data frame to an 'hce' object",
      "topics": [
        "as_hce.data.frame"
      ]
    },
    {
      "page": "as_hce.default",
      "title": "Coerce a data frame to an 'hce' object",
      "topics": [
        "as_hce.default"
      ]
    },
    {
      "page": "calcWINS",
      "title": "A generic function for calculating win statistics",
      "topics": [
        "calcWINS"
      ]
    },
    {
      "page": "calcWINS.data.frame",
      "title": "Win statistics calculation using a data frame",
      "topics": [
        "calcWINS.data.frame"
      ]
    },
    {
      "page": "calcWINS.formula",
      "title": "Win statistics calculation using formula syntax",
      "topics": [
        "calcWINS.formula"
      ]
    },
    {
      "page": "calcWINS.hce",
      "title": "Win statistics calculation for 'hce' objects",
      "topics": [
        "calcWINS.hce"
      ]
    },
    {
      "page": "calcWO",
      "title": "A generic function for calculating win odds",
      "topics": [
        "calcWO"
      ]
    },
    {
      "page": "calcWO.data.frame",
      "title": "Win odds calculation using a data frame",
      "topics": [
        "calcWO.data.frame"
      ]
    },
    {
      "page": "calcWO.formula",
      "title": "Win odds calculation using formula syntax",
      "topics": [
        "calcWO.formula"
      ]
    },
    {
      "page": "calcWO.hce",
      "title": "Win odds calculation for 'hce' objects",
      "topics": [
        "calcWO.hce"
      ]
    },
    {
      "page": "COVID19",
      "title": "COVID-19 ordinal scale dataset (full report).",
      "topics": [
        "COVID19"
      ]
    },
    {
      "page": "COVID19b",
      "title": "COVID-19 ordinal scale dataset (preliminary report).",
      "topics": [
        "COVID19b"
      ]
    },
    {
      "page": "COVID19plus",
      "title": "COVID-19 ordinal scale dataset for a combination therapy.",
      "topics": [
        "COVID19plus"
      ]
    },
    {
      "page": "deltaWO",
      "title": "A generic function for calculating win odds based on a threshold",
      "topics": [
        "deltaWO"
      ]
    },
    {
      "page": "deltaWO.adhce",
      "title": "Win odds calculation based on a threshold for 'adhce' objects",
      "topics": [
        "deltaWO.adhce"
      ]
    },
    {
      "page": "GLL",
      "title": "The Generalized Log-Logistic Distribution",
      "topics": [
        "dGLL",
        "GLL",
        "HGLL",
        "hGLL",
        "pGLL",
        "qGLL",
        "rGLL"
      ]
    },
    {
      "page": "hce",
      "title": "Helper function for 'hce' objects",
      "topics": [
        "hce"
      ]
    },
    {
      "page": "HCE1",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "topics": [
        "HCE1"
      ]
    },
    {
      "page": "HCE2",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "topics": [
        "HCE2"
      ]
    },
    {
      "page": "HCE3",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "topics": [
        "HCE3"
      ]
    },
    {
      "page": "HCE4",
      "title": "'HCE1', 'HCE2', 'HCE3', 'HCE4' datasets for 1000 patients with different treatment effects.",
      "topics": [
        "HCE4"
      ]
    },
    {
      "page": "IWP",
      "title": "Calculates patient-level individual win proportions",
      "topics": [
        "IWP"
      ]
    },
    {
      "page": "KHCE",
      "title": "Kidney Hierarchical Composite Endpoint dataset.",
      "topics": [
        "KHCE"
      ]
    },
    {
      "page": "minWO",
      "title": "Minimum detectable or WO for alternative hypothesis for given power (no ties)",
      "topics": [
        "minWO"
      ]
    },
    {
      "page": "plot.hce",
      "title": "A plot method for 'hce' objects",
      "topics": [
        "plot.hce"
      ]
    },
    {
      "page": "plot.hce_results",
      "title": "A print method for 'hce_results ' objects",
      "topics": [
        "plot.hce_results"
      ]
    },
    {
      "page": "powerWO",
      "title": "Power calculation for the win odds test (no ties)",
      "topics": [
        "powerWO"
      ]
    },
    {
      "page": "print.hce_results",
      "title": "A print method for 'hce_results' objects",
      "topics": [
        "print.hce_results"
      ]
    },
    {
      "page": "propWINS",
      "title": "Proportion of wins/losses/ties given the win odds and the win ratio",
      "topics": [
        "propWINS"
      ]
    },
    {
      "page": "regWO",
      "title": "A generic function for win odds regression",
      "topics": [
        "regWO"
      ]
    },
    {
      "page": "regWO.data.frame",
      "title": "Win Odds Regression Using a Data Frame",
      "topics": [
        "regWO.data.frame"
      ]
    },
    {
      "page": "regWO.formula",
      "title": "Win Odds Regression Using a Formula Syntax",
      "topics": [
        "regWO.formula"
      ]
    },
    {
      "page": "rweibullGF",
      "title": "Simulate random numbers from a Weibull distribution with gamma frailty",
      "topics": [
        "rweibullGF"
      ]
    },
    {
      "page": "simHCE",
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