Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention). Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a riskbased policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a casecontrol study if an estimate of the population outcome prevalence is available. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standardofcare and a risk model might be used to recommend that lowrisk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate crossvalidated curves using kfold crossvalidation is also provided.
Package details 


Author  Marshall Brown 
Maintainer  Marshall Brown <mdbrown@fredhutch.org> 
License  GPL2 
Version  1.6 
URL  http://mdbrown.github.io/rmda/ https://github.com/mdbrown/rmda 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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