Sampling with censored data: a practical guide

TL;DR

This paper provides a practical guide with algorithms and R code for sampling pseudo-random data from censored survival models, including an R package for easy implementation.

Contribution

It introduces a straightforward methodology for sampling censored data, including an R package, enhancing reproducibility and practical application in survival analysis.

Findings

Algorithms and R code for censored data sampling

Application to Weibull distribution models

Development of an R package for practitioners

Abstract

In this review, we present a simple guide for researchers to obtain pseudo-random samples with censored data. We focus our attention on the most common types of censored data, such as type I, type II, and random censoring. We discussed the necessary steps to sample pseudo-random values from long-term survival models where an additional cure fraction is informed. For illustrative purposes, these techniques are applied in the Weibull distribution. The algorithms and codes in R are presented, enabling the reproducibility of our study. Finally, we developed an R package that encapsulates these methodologies, providing researchers with practical tools for implementation.