average_km

Takes a list of multiply imputed datasets corresponding to a single tipping point
parameter and pools the Kaplan-Meier survival curves for a given treatment arm.
Uses log(-log) transformation and Rubin's rules for pooling across imputations.

internal

Implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

Ajmal Oodally

tipse

Tipping Point Analysis for Survival Endpoints

Craig Wang

Zheng Li

average_km function

<dl><dt>km_data</dt>
<dd>List of data frames, each containing one multiply imputed dataset for a tipping point.
Each data frame must contain columns <code>AVAL</code>, <code>EVENT</code>, <code>TRT01P</code>, <code>iter</code>, and a tipping point parameter depending on the method.</dd>
<dt>arm</dt>
<dd>Character string specifying the treatment arm to pool (must match <code>TRT01P</code> levels).</dd>
<dt>conf_level</dt>
<dd>Numeric. Confidence level for CIs (default = 0.95).</dd></dl>

Arguments

Average Kaplan-Meier Curves Across Multiple Imputed Datasets — average_km

<dl>

<dt>km_data</dt>
<dd>List of data frames, each containing one multiply imputed dataset for a tipping point.
Each data frame must contain columns <code>AVAL</code>, <code>EVENT</code>, <code>TRT01P</code>, <code>iter</code>, and a tipping point parameter depending on the method.</dd>


<dt>arm</dt>
<dd>Character string specifying the treatment arm to pool (must match <code>TRT01P</code> levels).</dd>


<dt>conf_level</dt>
<dd>Numeric. Confidence level for CIs (default = 0.95).</dd>

</dl>

average_km: Average Kaplan-Meier Curves Across Multiple Imputed Datasets

Description

Usage

Value

Arguments