trimByIptw: Remove subjects with a high IPTW

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

Description

Compute the inverse probability of treatment weights (IPTW) using the propensity scores, and remove subjects having a weight higher than the user-specified threshold.

Usage

trimByIptw(population, maxWeight = 10, estimator = "ate")

Arguments

population	A data frame with at least the three columns described below.
maxWeight	The maximum allowed IPTW.
estimator	The type of estimator. Options are estimator = "ate" for the average treatment effect, and estimator = "att"for the average treatment effect in the treated.

Details

The data frame should have the following three columns:

• rowId (numeric): A unique identifier for each row (e.g. the person ID).

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

• propensityScore (numeric): Propensity score.

Value

Returns a tibble with the same columns as the input, as well as a weights column containing the IPTW.

Examples

rowId <- 1:2000
treatment <- rep(0:1, each = 1000)
propensityScore <- c(runif(1000, min = 0, max = 1), runif(1000, min = 0, max = 1))
data <- data.frame(rowId = rowId, treatment = treatment, propensityScore = propensityScore)
result <- trimByIptw(data)

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