computePsAuc: Compute the area under the ROC curve

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

Compute the area under the ROC curve

Description

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

Usage

computePsAuc(data, confidenceIntervals = FALSE, maxRows = 1e+05)

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)
maxRows	Maximum number of rows to use. If the number of rows is larger, a random sample will be taken. This can increase speed, with minor cost to precision. Set to 0 to use all data.

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)

OHDSI/CohortMethod documentation built on Feb. 9, 2024, 9:01 a.m.