trim_weights_symmetric: Symmetric Propensity Score Trimming (Crump Extension)

In PSsurvival: Propensity Score Methods for Survival Analysis

Symmetric Propensity Score Trimming (Crump Extension)

Description

Performs symmetric trimming based on minimum propensity score across all treatment groups. Implements the Crump extension to multiple treatments as described in Yoshida et al. (2019).

Usage

trim_weights_symmetric(ps_result, data, treatment_var, delta = NULL)

Arguments

ps_result	A list returned by estimate_ps().
data	A data.frame containing the treatment variable.
treatment_var	A character string specifying the name of the treatment variable in data.
delta	Numeric trimming threshold in (0, 1/J] where J is the number of treatment levels. Default is NULL, which uses recommended values: 0.1 for binary treatment, 0.067 for 3 groups, 1/(2*J) for J >= 4.

Details

The symmetric trimming rule retains observation i if:

\min_j\{e_{ji}\} \geq \delta

For binary treatment (J=2), this reduces to: e(X) \in [\delta, 1-\delta].

Value

A logical vector of length n, where TRUE indicates the observation should be kept and FALSE indicates it should be trimmed.

References

Yoshida, K., et al. (2019). Multinomial extension of propensity score trimming methods: A simulation study. American Journal of Epidemiology, 188(3), 609-616.

PSsurvival documentation built on Dec. 9, 2025, 9:07 a.m.