{
  "_id": "6a101c35acfb0bcc41c87a91",
  "Package": "sievePH",
  "Type": "Package",
  "Title": "Sieve Analysis Methods for Proportional Hazards Models",
  "Version": "1.2",
  "Date": "2024-07-01",
  "Authors@R": "c(person(\"Michal\", \"Juraska\", email = \"mjuraska@fredhutch.org\", role = c(\"aut\", \"cre\")), person(\"Li\", \"Li\", email = \"lli4@fredhutch.org\", role = \"ctb\"), person(\"Stephanie\", \"Wu\", email = \"swu18@uw.edu\", role = \"ctb\"))",
  "Description": "Implements a suite of semiparametric and nonparametric\nkernel-smoothed estimation and testing procedures for\ncontinuous mark-specific stratified hazard ratio\n(treatment/placebo) models in a randomized treatment efficacy\ntrial with a time-to-event endpoint. Semiparametric methods,\nallowing multivariate marks, are described in Juraska M and\nGilbert PB (2013), Mark-specific hazard ratio model with\nmultivariate continuous marks: an application to vaccine\nefficacy. Biometrics 69(2):328-337 <doi:10.1111/biom.12016>,\nand in Juraska M and Gilbert PB (2016), Mark-specific hazard\nratio model with missing multivariate marks. Lifetime Data\nAnalysis 22(4):606-25 <doi:10.1007/s10985-015-9353-9>.\nNonparametric kernel-smoothed methods, allowing univariate\nmarks only, are described in Sun Y and Gilbert PB (2012),\nEstimation of stratified mark‐specific proportional hazards\nmodels with missing marks. Scandinavian Journal of Statistics},\n39(1):34-52 <doi:10.1111/j.1467-9469.2011.00746.x>, and in\nGilbert PB and Sun Y (2015), Inferences on relative failure\nrates in stratified mark-specific proportional hazards models\nwith missing marks, with application to human immunodeficiency\nvirus vaccine efficacy trials. Journal of the Royal Statistical\nSociety Series C: Applied Statistics, 64(1):49-73\n<doi:10.1111/rssc.12067>. Both semiparametric and nonparametric\napproaches consider two scenarios: (1) the mark is fully\nobserved in all subjects who experience the event of interest,\nand (2) the mark is subject to missingness-at-random in\nsubjects who experience the event of interest. For models with\nmissing marks, estimators are implemented based on (i) inverse\nprobability weighting (IPW) of complete cases (for the\nsemiparametric framework), and (ii) augmentation of the IPW\nestimating functions by leveraging correlations between the\nmark and auxiliary data to 'impute' the augmentation term for\nsubjects with missing marks (for both the semiparametric and\nnonparametric framework). The augmented IPW estimators are\ndoubly robust and recommended for use with incomplete mark\ndata. The semiparametric methods make two key assumptions: (i)\nthe time-to-event is assumed to be conditionally independent of\nthe mark given treatment, and (ii) the weight function in the\nsemiparametric density ratio/biased sampling model is assumed\nto be exponential. Diagnostic testing procedures for evaluating\nvalidity of both assumptions are implemented. Summary and\nplotting functions are provided for estimation and inferential\nresults.",
  "URL": "https://github.com/mjuraska/sievePH",
  "BugReports": "https://github.com/mjuraska/sievePH/issues",
  "License": "GPL-2",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.1",
  "Config/pak/sysreqs": "cmake make libicu-dev",
  "Repository": "https://mjuraska.r-universe.dev",
  "Date/Publication": "2026-01-14 17:28:09 UTC",
  "RemoteUrl": "https://github.com/mjuraska/sieveph",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-14 08:36:47 UTC",
    "User": "root"
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  "Author": "Michal Juraska [aut, cre],\nLi Li [ctb],\nStephanie Wu [ctb]",
  "Maintainer": "Michal Juraska <mjuraska@fredhutch.org>",
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  "_created": "2026-05-14T08:36:47.000Z",
  "_published": "2026-05-22T09:04:53.465Z",
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    "message": "Add warning if maximum number of iterations reached without convergence\n",
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  "_selfowned": true,
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  "_updates": [
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      "week": "2025-20",
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    "name": "Michal Juraska",
    "description": "A biostatistician at Fred Hutch"
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  "_topics": [
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    "cpp",
    "openmp"
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  "_assets": [
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    "extra/citation.html",
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      "date": "2019-02-26"
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      "date": "2019-12-06"
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      "date": "2023-02-03"
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  "_exports": [
    "ggplot_sieve",
    "kernel_sievePH",
    "kernel_sievePHaipw",
    "sievePH",
    "sievePHaipw",
    "sievePHipw",
    "testDensRatioGOF",
    "testIndepTimeMark"
  ],
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    {
      "page": "ggplot_sieve",
      "title": "Plotting Univariate Mark-Specific Proportional Hazards Model Fits Using 'ggplot'",
      "topics": [
        "ggplot_sieve"
      ]
    },
    {
      "page": "kernel_sievePH",
      "title": "Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model with a Univariate Continuous Mark, Fully Observed in All Failures.",
      "topics": [
        "kernel_sievePH"
      ]
    },
    {
      "page": "kernel_sievePHaipw",
      "title": "Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model with a Univariate Continuous Mark, Missing-at-Random in Some Failures",
      "topics": [
        "kernel_sievePHaipw"
      ]
    },
    {
      "page": "plot.summary.sievePH",
      "title": "Plotting Mark-Specific Proportional Hazards Model Fits",
      "topics": [
        "plot.summary.sievePH"
      ]
    },
    {
      "page": "sievePH",
      "title": "Semiparametric Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Fully Observed in All Failures",
      "topics": [
        "sievePH"
      ]
    },
    {
      "page": "sievePHaipw",
      "title": "Semiparametric Augmented Inverse Probability Weighted Complete-Case Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Missing-at-Random in Some Failures",
      "topics": [
        "sievePHaipw"
      ]
    },
    {
      "page": "sievePHipw",
      "title": "Semiparametric Inverse Probability Weighted Complete-Case Estimation of Coefficients in a Mark-Specific Proportional Hazards Model with a Multivariate Continuous Mark, Missing-at-Random in Some Failures",
      "topics": [
        "sievePHipw"
      ]
    },
    {
      "page": "summary.kernel_sievePH",
      "title": "Summarizing Nonparametric Kernel-Smoothed Stratified Mark-Specific Proportional Hazards Model Fits",
      "topics": [
        "print.summary.kernel_sievePH",
        "summary.kernel_sievePH"
      ]
    },
    {
      "page": "summary.sievePH",
      "title": "Summarizing Mark-Specific Proportional Hazards Model Fits",
      "topics": [
        "print.summary.sievePH",
        "summary.sievePH"
      ]
    },
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      "page": "testDensRatioGOF",
      "title": "Goodness-of-Fit Test of the Validity of a Univariate or Multivariate Mark Density Ratio Model",
      "topics": [
        "testDensRatioGOF"
      ]
    },
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      "page": "testIndepTimeMark",
      "title": "Kolmogorov-Smirnov-Type Test of Conditional Independence between the Time-to-Event and a Multivariate Mark Given Treatment",
      "topics": [
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