computePsAuc: Compute the area under the ROC curve

In escott12/CohortMethod: New-User Cohort Method with Large Scale Propensity and Outcome Models

Description

Compute the area under the ROC curve of the propensity score.

Usage

computePsAuc(data, confidenceIntervals = FALSE)

Arguments

data	A data frame with at least the two columns described below
confidenceIntervals	Compute 95 percent confidence intervals (computationally expensive for large data sets)

Details

The data frame should have a least the following two columns:

• treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.

• propensityScore (numeric): Propensity score.

Value

A tibble holding the AUC and its 95 percent confidence interval

Examples

treatment <- rep(0:1, each = 100)
propensityScore <- c(rnorm(100, mean = 0.4, sd = 0.25), rnorm(100, mean = 0.6, sd = 0.25))
data <- data.frame(treatment = treatment, propensityScore = propensityScore)
data <- data[data$propensityScore > 0 & data$propensityScore < 1, ]
computePsAuc(data)

escott12/CohortMethod documentation built on Dec. 20, 2021, 6:37 a.m.