computePsAuc computes the area under the ROC curve of the propensity score

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)

Value

A data frame holding the AUC and its 95 percent confidence interval

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

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)
#> [1] 0.6727075