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  "Title": "Virtual Patient Simulation by Copula Invariance Property",
  "Version": "0.0.1",
  "Date": "2022-08-06",
  "Authors@R": "c(person('Pei-Shan', 'Yen', email = 'peishan0824@gmail.com', role = c('aut', 'cre'), comment = c(ORCID = '0000-0001-7386-0552')),\nperson('Xuemin', 'Gu', email = 'xuemin.gu@abbvie.com', role = 'ctb'),\nperson('Jenny', 'Jiao', email = 'jenny.jiao@abbvie.com', role = 'ctb'),\nperson('Jane', 'Zhang', email = 'jane.zhang@abbvie.com', role = 'ctb'))",
  "Maintainer": "Pei-Shan Yen <peishan0824@gmail.com>",
  "Description": "To optimize clinical trial designs and data analysis\nmethods consistently through trial simulation, we need to\nsimulate multivariate mixed-type virtual patient data\nindependent of designs and analysis methods under evaluation.\nTo make the outcome of optimization more realistic, relevant\nempirical patient level data should be utilized when it’s\navailable. However, a few problems arise in simulating trials\nbased on small empirical data, where the underlying marginal\ndistributions and their dependence structure cannot be\nunderstood or verified thoroughly due to the limited sample\nsize. To resolve this issue, we use the copula invariance\nproperty, which can generate the joint distribution without\nmaking a strong parametric assumption. The function copula.sim\ncan generate virtual patient data with optional data validation\nmethods that are based on energy distance and ball divergence\nmeasurement. The function compare.copula.sim can conduct\ncomparison of marginal mean and covariance of simulated data.\nTo simulate patient-level data from a hypothetical treatment\narm that would perform differently from the observed data, the\nfunction new.arm.copula.sim can be used to generate new\nmultivariate data with the same dependence structure of the\noriginal data but with a shifted mean vector.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/psyen0824/copulaSim",
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    {
      "page": "copulaSim-package",
      "title": "copulaSim: Virtual Patient Simulation by Copula Invariance Property",
      "topics": [
        "copulaSim-package",
        "copulaSim"
      ]
    },
    {
      "page": "compare.copula.sim",
      "title": "Performing the comparison between empirical data and multiple simulated datasets.",
      "topics": [
        "compare.copula.sim"
      ]
    },
    {
      "page": "copula.sim",
      "title": "To generate simulated datasets from empirical data by utilizing the copula invariance property.",
      "topics": [
        "copula.sim"
      ]
    },
    {
      "page": "data.diff.test",
      "title": "Performing the hypothesis test to compare the difference between the empirical data and the simulated data",
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        "data.diff.test"
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    },
    {
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      "title": "Obtaining the inverse of marginal empirical cumulative distribution (ECDF)",
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        "ecdf.inv"
      ]
    },
    {
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      "title": "Converting data.simul in a copula.sim object into a list of wide-form matrices",
      "topics": [
        "extract.data.sim"
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      "page": "new.arm.copula.sim",
      "title": "Simulating new multivariate datasets with shifted mean vector from existing empirical data",
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      "source": "introduction.Rmd",
      "filename": "introduction.html",
      "title": "Introduction to R package copulaSim",
      "author": "Pei-Shan Yen",
      "engine": "knitr::rmarkdown",
      "headings": [
        "What is the usage of the R package copulaSim?",
        "Why do we need to perform virtual patient simulation?",
        "The challenges faced when simulating small data",
        "Solution: copula invariance property",
        "Sklar's Theorem",
        "Why can the copula function be employed to generate joint distribution?",
        "The implementation of copula into a new R package",
        "The demonstration of R Package copulaSim",
        "Generate empirical data:",
        "Assume that the single-arm, 5-dimensional empirical data follows multivariate normal data, and let the sample size of empirical data be 30.",
        "Load package copulaSim",
        "Use function copula.sim to generate ONE simulated dataset for the emperical data",
        "Use function extract.data.sim to convert simulated data into a list of wide-form matrices",
        "Use function compare.copula.sim to perform the comparison between empirical data and multiple simulated datasets.",
        "Employ function new.arm.copula.sim to simulate new multivariate datasets with shifted mean vector from existing empirical data.",
        "Acknowledgement"
      ],
      "created": "2022-08-02 20:10:28",
      "modified": "2022-08-20 16:20:46",
      "commits": 9
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