Compute the area under the ROC curve of the propensity score.
computePsAuc(data, confidenceIntervals = FALSE)
A data frame with at least the two columns described below
Compute 95 percent confidence intervals (computationally expensive for large data sets)
A tibble holding the AUC and its 95 percent confidence interval
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.
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)#>  0.6727075