trim_weights_symmetric

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).

internal

Implements propensity score weighting methods for estimating
counterfactual survival functions, marginal hazard ratios, and
weighted Kaplan-Meier and cumulative risk curves in observational
studies with time-to-event outcomes. Supports binary and multiple
treatment groups with inverse probability of treatment weighting (IPW),
overlap weighting (OW), and average treatment effect on the treated
(ATT). Includes symmetric trimming (Crump extension) for extreme
propensity scores. Variance estimation via analytical M-estimation or
bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>,
Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022)
<doi:10.1093/aje/kwac043>.

Chengxin Yang

PSsurvival

Propensity Score Methods for Survival Analysis

Chao Cheng

Fan Li

trim_weights_symmetric function

<dl><dt>ps_result</dt>
<dd>A list returned by <code>estimate_ps()</code>.</dd>
<dt>data</dt>
<dd>A data.frame containing the treatment variable.</dd>
<dt>treatment_var</dt>
<dd>A character string specifying the name of the treatment
variable in <code>data</code>.</dd>
<dt>delta</dt>
<dd>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 &gt;= 4.</dd></dl>

Arguments

Symmetric Propensity Score Trimming (Crump Extension) — trim_weights_symmetric

<dl>

<dt>ps_result</dt>
<dd>A list returned by <code>estimate_ps()</code>.</dd>


<dt>data</dt>
<dd>A data.frame containing the treatment variable.</dd>


<dt>treatment_var</dt>
<dd>A character string specifying the name of the treatment
variable in <code>data</code>.</dd>


<dt>delta</dt>
<dd>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 &gt;= 4.</dd>

</dl>

trim_weights_symmetric: Symmetric Propensity Score Trimming (Crump Extension)

Description

Usage

Value

Arguments

Details

References