validate_data_variables

Performs all variable-specific validation checks on treatment, time, event,
and covariates. Only stops execution if critical errors are found.

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

validate_data_variables function

<dl><dt>data</dt>
<dd>A data.frame containing the analysis data.</dd>
<dt>treatment_var</dt>
<dd>Character string of treatment variable name.</dd>
<dt>ps_formula</dt>
<dd>Formula object for propensity score model.</dd>
<dt>censor_formula</dt>
<dd>Formula object for censoring model.</dd></dl>

Arguments

Validate Data Variables — validate_data_variables

<dl>

<dt>data</dt>
<dd>A data.frame containing the analysis data.</dd>


<dt>treatment_var</dt>
<dd>Character string of treatment variable name.</dd>


<dt>ps_formula</dt>
<dd>Formula object for propensity score model.</dd>


<dt>censor_formula</dt>
<dd>Formula object for censoring model.</dd>

</dl>

validate_data_variables: Validate Data Variables

Description

Usage

Value

Arguments