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  "Title": "Nonparametric Analysis of Clustered Multistate Processes",
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  "Description": "Nonparametric estimation of population-averaged transition\nprobabilities, with cluster-bootstrap pointwise confidence\nintervals, simultaneous confidence bands, and two-sample\nKolmogorov-Smirnov-type tests for clustered or independent\nmultistate process data. Estimation follows Bakoyannis (2021)\n<doi:10.1111/biom.13327>; two-sample inference for the\ncluster-randomized and independent-samples designs follows\nBakoyannis and Bandyopadhyay (2022)\n<doi:10.1007/s10463-021-00819-x>. Both methods use the\nworking-independence Aalen-Johansen estimator. The package\nsupports both progressive (acyclic) and non-monotone (e.g.,\nillness-death with recovery) multistate processes, right\ncensoring, left truncation, and informative cluster size. The\nuser supplies data in interval format (one row per\nmutually-exclusive time interval per subject) and interacts\nwith the package through a single formula-based function,\npatp().",
  "License": "GPL-3",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/gbakoyannis/clusteredMSM",
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  "Author": "Giorgos Bakoyannis [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-2789-2497>)",
  "Maintainer": "Giorgos Bakoyannis <gbakogia@iu.edu>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-05-27 19:20:08 UTC",
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    "cluster_boot",
    "confidence_band",
    "cut_at_lm",
    "fit_chaz",
    "intervals_to_long",
    "ks_pvalue",
    "msm",
    "patp",
    "prodint_AJ",
    "state_at",
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    "validate_intervals"
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      "name": "example_msm",
      "title": "Synthetic Clustered Illness-Death-with-Recovery Data",
      "object": "example_msm",
      "class": [
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      ],
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        "id",
        "cluster",
        "treatment",
        "Tstart",
        "Tstop",
        "Sstart",
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      "table": true,
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    {
      "page": "ci_cloglog",
      "title": "Pointwise Confidence Intervals via Complementary Log-Log Transformation",
      "topics": [
        "ci_cloglog"
      ]
    },
    {
      "page": "cluster_boot",
      "title": "Generic Cluster Bootstrap Engine",
      "topics": [
        "cluster_boot"
      ]
    },
    {
      "page": "confidence_band",
      "title": "Simultaneous Confidence Band via Cluster-Bootstrap Quantile",
      "topics": [
        "confidence_band"
      ]
    },
    {
      "page": "cut_at_lm",
      "title": "Truncate a Long-Format Multistate Dataset at a Landmark Time",
      "topics": [
        "cut_at_lm"
      ]
    },
    {
      "page": "example_msm",
      "title": "Synthetic Clustered Illness-Death-with-Recovery Data",
      "topics": [
        "example_msm"
      ]
    },
    {
      "page": "fit_chaz",
      "title": "Fit Cox Model Stratified by Transition and Return Cumulative Hazards",
      "topics": [
        "fit_chaz"
      ]
    },
    {
      "page": "intervals_to_long",
      "title": "Convert Validated Interval Data to Long Multistate Format",
      "topics": [
        "intervals_to_long"
      ]
    },
    {
      "page": "ks_pvalue",
      "title": "Two-Sample Kolmogorov-Smirnov-Type Test via Cluster Bootstrap",
      "topics": [
        "ks_pvalue"
      ]
    },
    {
      "page": "msm",
      "title": "Construct a Multistate Interval Object for Use in patp() Formulas",
      "topics": [
        "msm"
      ]
    },
    {
      "page": "patp",
      "title": "Population-Averaged Transition Probabilities for Multistate Process Data",
      "topics": [
        "patp"
      ]
    },
    {
      "page": "print.patp",
      "title": "Print Method for patp Objects",
      "topics": [
        "print.patp"
      ]
    },
    {
      "page": "prodint_AJ",
      "title": "Aalen-Johansen Product Integral for General Multistate Processes",
      "topics": [
        "prodint_AJ"
      ]
    },
    {
      "page": "state_at",
      "title": "Identify Each Subject's State at a Given Time",
      "topics": [
        "state_at"
      ]
    },
    {
      "page": "summary.patp",
      "title": "Summary Method for patp Objects",
      "topics": [
        "summary.patp"
      ]
    },
    {
      "page": "trans_mat",
      "title": "Build a Transition Matrix for a Multistate Process",
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      ]
    }
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        "Input data format",
        "A progressive illness-death example",
        "One-sample estimation",
        "Two-sample comparison (estimation + test in one call)",
        "Recovery models (non-monotone processes)",
        "Landmark variant",
        "Inverse-cluster-size weighting",
        "What B = 0 does",
        "References"
      ],
      "created": "2026-05-27 19:20:08",
      "modified": "2026-05-27 19:20:08",
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