propensity_score_methods: Estimate Treatment Effects using Propensity Score Matching

In debiasedTrialEmulation: Pipeline for Debiased Target Trial Emulation

Estimate Treatment Effects using Propensity Score Matching

Description

Computes effect estimates using propensity score matching to reduce confounding.

Computes effect estimates using propensity score stratification to adjust for confounding.

Computes effect estimates using propensity score weighting to balance covariates between treatment groups.

Usage

estEffect_matching(form, data, yvars, ncovars, distance, outcome_measure)

estEffect_stratification(form, data, yvars, ncovars, distance, outcome_measure)

estEffect_weighting(form, data, yvars, ncovars, distance, outcome_measure)

Arguments

form	A formula specifying the treatment assignment model.
data	A dataset containing covariates and treatment assignment.
yvars	A character vector of outcome variable names.
ncovars	A character vector of negative control outcome variable names.
distance	The method for estimating propensity scores ("glm").
outcome_measure	The outcome measure to estimate: "RR" (Risk Ratio), "OR" (Odds Ratio), or "HR" (Hazard Ratio).

Value

List of components

List of components

List of components

debiasedTrialEmulation documentation built on June 8, 2025, 9:42 p.m.