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  "Title": "Interval-Censored Sequence Kernel Association Test",
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  "Description": "Implements the Interval-Censored Sequence Kernel\nAssociation (ICSKAT) test for testing the association between\ninterval-censored time-to-event outcomes and groups of single\nnucleotide polymorphisms (SNPs). Interval-censored\ntime-to-event data occur when the event time is not known\nexactly but can be deduced to fall within a given interval. For\nexample, some medical conditions like bone mineral density\ndeficiency are generally only diagnosed at clinical visits. If\na patient goes for clinical checkups yearly and is diagnosed\nat, say, age 30, then the onset of the deficiency is only known\nto fall between the date of their age 29 checkup and the date\nof the age 30 checkup. Interval-censored data include right-\nand left-censored data as special cases. This package also\nimplements the interval-censored Burden test and the ICSKATO\ntest, which is the optimal combination of the ICSKAT and Burden\ntests. Please see the vignette for a quickstart guide. The\npaper describing these methods is \" Inference for Set-Based\nEffects in Genetic Association Studies with Interval-Censored\nOutcomes\" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L\n(Biometrics 2023, <doi:10.1111/biom.13636>).",
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      "page": "ACAT",
      "title": "Aggregated Cauchy Association Test",
      "topics": [
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      "title": "chiSqMatchFast.R",
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      "title": "coxphFn",
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      "title": "Called by gen_IC_data() to turn the actual outcome times and observation times into interval-censored outcomes for each subject. Apply this with mapply over a data.frame of visit times, pass in the exact times.",
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        "fIntegrate"
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      "title": "fIntegrateLiu.R",
      "topics": [
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      "title": "gen_IC_data.R",
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      "page": "ICsingleSNP",
      "title": "ICsingleSNP.R",
      "topics": [
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    {
      "page": "ICskat",
      "title": "ICSKAT.R",
      "topics": [
        "ICskat"
      ]
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      "title": "ICSKAT_fit_null_PO.R",
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      "topics": [
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