{ "_id": "6a197d09acfb0bcc41df6cfb", "Package": "rmda", "Type": "Package", "Title": "Risk Model Decision Analysis", "Version": "1.6", "Date": "2018-07-17", "Author": "Marshall Brown", "Maintainer": "Marshall Brown ", "Description": "Provides tools to evaluate the value of using a risk\nprediction instrument to decide treatment or intervention\n(versus no treatment or intervention). Given one or more risk\nprediction instruments (risk models) that estimate the\nprobability of a binary outcome, rmda provides functions to\nestimate and display decision curves and other figures that\nhelp assess the population impact of using a risk model for\nclinical decision making. Here, \"population\" refers to the\nrelevant patient population. Decision curves display estimates\nof the (standardized) net benefit over a range of probability\nthresholds used to categorize observations as 'high risk'. The\ncurves help evaluate a treatment policy that recommends\ntreatment for patients who are estimated to be 'high risk' by\ncomparing the population impact of a risk-based policy to\n\"treat all\" and \"treat none\" intervention policies. Curves can\nbe estimated using data from a prospective cohort. In\naddition, rmda can estimate decision curves using data from a\ncase-control study if an estimate of the population outcome\nprevalence is available. Version 1.4 of the package provides\nan alternative framing of the decision problem for situations\nwhere treatment is the standard-of-care and a risk model might\nbe used to recommend that low-risk patients (i.e., patients\nbelow some risk threshold) opt out of treatment. Confidence\nintervals calculated using the bootstrap can be computed and\ndisplayed. A wrapper function to calculate cross-validated\ncurves using k-fold cross-validation is also provided.", "License": "GPL-2", "URL": "http://mdbrown.github.io/rmda/, https://github.com/mdbrown/rmda", "LazyData": "TRUE", "RoxygenNote": "6.0.1", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://mdbrown.r-universe.dev", "Date/Publication": "2018-10-16 21:19:56 UTC", "RemoteUrl": "https://github.com/mdbrown/rmda", "RemoteRef": "HEAD", "RemoteSha": "84a11aff0e21793a834e1127968da644739c6bed", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-29 10:40:59 UTC", "User": "root" }, "MD5sum": "89ec63a0d39c2d35e130d69709325886", "_user": "mdbrown", "_type": "src", "_file": "rmda_1.6.tar.gz", "_fileid": "1a43308b1acf9e74a6b1ea779a471fd77cfa0ca09373b98dca467509fa5710de", "_filesize": 172716, "_sha256": "1a43308b1acf9e74a6b1ea779a471fd77cfa0ca09373b98dca467509fa5710de", "_created": "2026-05-29T10:40:59.000Z", "_published": "2026-05-29T11:48:25.412Z", "_distro": "noble", "_jobs": [ { 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"type": "user", "name": "Marshall Brown" }, "_downloads": { "count": 844, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/rmda" }, "_mentions": 131, "_devurl": "https://github.com/mdbrown/rmda", "_searchresults": 128, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/rmda.html", "manual.pdf" ], "_homeurl": "https://github.com/mdbrown/rmda", "_realowner": "mdbrown", "_cranurl": true, "_releases": [ { "version": "1.4", "date": "2017-08-01" }, { "version": "1.5", "date": "2018-03-21" }, { "version": "1.6", "date": "2018-07-17" } ], "_exports": [ "Add_CostBenefit_Axis", "cv_decision_curve", "decision_curve", "plot_clinical_impact", "plot_decision_curve", "plot_roc_components" ], "_datasets": [ { "name": "dcaData", "title": "Simulated dataset for package 'DecisionCurve'", "object": "dcaData", 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"topics": [ "dcaData_cc" ] }, { "page": "decision_curve", "title": "Calculate net benefit/decision curves", "topics": [ "decision_curve" ] }, { "page": "plot_clinical_impact", "title": "Plot the clinical impact curve from a DecisionCurve object.", "topics": [ "plot_clinical_impact" ] }, { "page": "plot_decision_curve", "title": "Plot the net benefit curves from a decision_curve object or many decision_curve objects", "topics": [ "plot_decision_curve" ] }, { "page": "plot_roc_components", "title": "Plot the components of a ROC curve by the high risk thresholds.", "topics": [ "plot_roc_components" ] }, { "page": "summary.decision_curve", "title": "Displays a useful description of a decision_curve object", "topics": [ "summary.decision_curve" ] } ], "_readme": "https://github.com/mdbrown/rmda/raw/HEAD/README.md", "_rundeps": [ "caret", "class", "cli", "clock", "codetools", "cpp11", "data.table", "diagram", "digest", "dplyr", "e1071", "farver", "foreach", "future", "future.apply", 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