generate_boot_indices

Helper function to generate bootstrap sample indices for either full sample
resampling (observational studies) or stratified resampling within treatment
groups (randomized controlled trials with fixed allocation).

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

generate_boot_indices function

<dl><dt>data</dt>
<dd>Data frame to bootstrap from.</dd>
<dt>treatment_var</dt>
<dd>Name of treatment variable.</dd>
<dt>boot_level</dt>
<dd>Bootstrap sampling level: "full" (default) samples from
entire dataset, "strata" samples within each treatment group preserving
group sizes.</dd></dl>

Arguments

Generate Bootstrap Sample Indices — generate_boot_indices

<dl>

<dt>data</dt>
<dd>Data frame to bootstrap from.</dd>


<dt>treatment_var</dt>
<dd>Name of treatment variable.</dd>


<dt>boot_level</dt>
<dd>Bootstrap sampling level: "full" (default) samples from
entire dataset, "strata" samples within each treatment group preserving
group sizes.</dd>

</dl>

generate_boot_indices: Generate Bootstrap Sample Indices

Description

Usage

Value

Arguments