codebreak200

Based on re-constructed Kaplan-Meier plot from CodeBreak 200 trial (de Langen et al., 2023)

datasets

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

codebreak200 function

A data frame with 345 rows and 5 columns:<dl>
<dt>SUBJID</dt>
<dd>Dummy patient ID</dd><dt>TRT01P</dt>
<dd>Treatment arm (Sotorasib or Docetaxel)</dd><dt>AVAL</dt>
<dd>PFS time in days</dd><dt>EVENT</dt>
<dd>Indicator for PFS event</dd><dt>CNSRRS</dt>
<dd>Censoring reason (Early dropout or Other)</dd><dt>MAXAVAL</dt>
<dd>Maximum potential survival time, duration between randomization to data cut-off</dd>
</dl>

Format

Patient level data from dummy trial — codebreak200

A data frame with 345 rows and 5 columns:<dl>
<dt>SUBJID</dt>
<dd>Dummy patient ID</dd>

<dt>TRT01P</dt>
<dd>Treatment arm (Sotorasib or Docetaxel)</dd>

<dt>AVAL</dt>
<dd>PFS time in days</dd>

<dt>EVENT</dt>
<dd>Indicator for PFS event</dd>

<dt>CNSRRS</dt>
<dd>Censoring reason (Early dropout or Other)</dd>

<dt>MAXAVAL</dt>
<dd>Maximum potential survival time, duration between randomization to data cut-off</dd>


</dl>

codebreak200: Patient level data from dummy trial

Description

Usage

Arguments

Format