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  "Title": "Causal Inference and Prediction in Cohort-Based Analyses",
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  "Authors@R": "c(\nperson(\"Yohann\", \"Foucher\", email = \"Yohann.Foucher@univ-poitiers.fr\", role = c(\"aut\", \"cre\"), comment =\nc(ORCID = \"0000-0003-0330-7457\")) )",
  "Description": "Numerous functions for cohort-based analyses, either for\nprediction or causal inference. For causal inference, it\nincludes Inverse Probability Weighting and G-computation for\nmarginal estimation of an exposure effect when confounders are\nexpected. We deal with binary outcomes, times-to-events,\ncompeting events, and multi-state data. For multistate data,\nsemi-Markov model with interval censoring may be considered,\nand we propose the possibility to consider the excess of\nmortality related to the disease compared to reference lifetime\ntables. For predictive studies, we propose a set of functions\nto estimate time-dependent receiver operating characteristic\n(ROC) curves with the possible consideration of right-censoring\ntimes-to-events or the presence of confounders. Finally,\nseveral functions are available to assess time-dependent ROC\ncurves or survival curves from aggregated data.",
  "License": "GPL (>=2)",
  "LazyLoad": "yes",
  "NeedsCompilation": "no",
  "Maintainer": "Yohann Foucher <Yohann.Foucher@univ-poitiers.fr>",
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  "Repository": "https://foucher-y.r-universe.dev",
  "Date/Publication": "2025-02-21 08:47:25 UTC",
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