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  "Package": "bonsaiforest",
  "Title": "Shrinkage Based Forest Plots",
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  "Date": "2024-09-25",
  "Authors@R": "c(\nperson(\"Mar\", \"Vazquez Rabunal\", , \"mar.vazquez_rabunal@roche.com\", role = \"aut\"),\nperson(\"Daniel\", \"Sabanés Bové\", , \"daniel.sabanes_bove@roche.com\", role = \"aut\"),\nperson(\"Marcel\", \"Wolbers\", , \"marcel.wolbers@roche.com\", role = \"aut\"),\nperson(\"Isaac\", \"Gravestock\", , \"isaac.gravestock@roche.com\", role = \"cre\"),\nperson(\"F. Hoffmann-La Roche AG\", role = c(\"cph\", \"fnd\"))\n)",
  "Description": "Subgroup analyses are routinely performed in clinical\ntrial analyses. From a methodological perspective, two key\nissues of subgroup analyses are multiplicity (even if only\npredefined subgroups are investigated) and the low sample sizes\nof subgroups which lead to highly variable estimates, see e.g.\nYusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This\npackage implements subgroup estimates based on Bayesian\nshrinkage priors, see Carvalho et al (2019)\n<https://proceedings.mlr.press/v5/carvalho09a.html>. In\naddition, estimates based on penalized likelihood inference are\navailable, based on Simon et al (2011)\n<doi:10.18637/jss.v039.i05>. The corresponding shrinkage based\nforest plots address the aforementioned issues and can\ncomplement standard forest plots in practical clinical trial\nanalyses.",
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