trim_weights_asymmetric: Asymmetric Propensity Score Trimming (Sturmer Extension)

In PSsurvival: Propensity Score Methods for Survival Analysis

Asymmetric Propensity Score Trimming (Sturmer Extension)

Description

Performs asymmetric (percentile-based) trimming using within-group percentile thresholds. Implements the Sturmer extension to multiple treatments as described in Yoshida et al. (2019).

Usage

trim_weights_asymmetric(ps_result, data, treatment_var, alpha = 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.
alpha	Numeric percentile threshold in (0, 0.5). Default is NULL, which uses recommended values: 0.05 for binary treatment, 0.033 for 3 groups, 1/(2*J) for J >= 4.

Details

The asymmetric trimming rule retains observation i if:

e_{ji} \geq F^{-1}_{e_{ji}|A_i=j}(\alpha|j) \text{ for all } j

where F^{-1}_{e_{ji}|A_i=j}(\alpha|j) is the \alpha-percentile of propensity scores e_{ji} among individuals who actually received treatment j.

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.