mdbrown/AIPWmeasures: Estimate measures of predictive accuracy using augmented inverse probability weights for two-phase biomarker validation studies
Version 0.1.0

This package includes a function `AIPWmeasures` that calculates standard measures of predictive accuracy commonly used in biomarker validation studies. More specifically, this function calculates estimates using data from two-phase sampling designs ('case-cohort' and 'nested case-control') using two different methods, a standard ipw estimator (true ipw) and a novel method (augmented ipw) that has been shown to be more efficient in some contexts. See the manuscript "Improving Efficiency in Biomarker Incremental Value Evaluation under Two-phase Study Designs" by Zheng et. al. for more details.

Getting started

Package details

AuthorMarshall Brown and Yingye Zheng
MaintainerMarshall Brown <[email protected]>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
mdbrown/AIPWmeasures documentation built on May 22, 2017, 2:08 a.m.