`computePsAuc`

computes the area under the ROC curve of the propensity score

computePsAuc(data, confidenceIntervals = FALSE)

data | A data frame with at least the two columns described below |
---|---|

confidenceIntervals | Compute 95 percent confidence intervals (computationally expensive for large data sets) |

A data frame 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)#> [1] 0.6727075