Survival Data Simulation With the R Package rsurv

TL;DR

The paper introduces rsurv, an R package for flexible and comprehensive survival data simulation from various models and distributions, utilizing dplyr verbs for ease of use.

Contribution

It presents a novel R package that enables simulation of survival data across multiple models and distributions with advanced features and user-friendly formula interface.

Findings

Supports a wide range of regression models including AFT, PH, PO, AH, YP, and EH.

Allows simulation from unlimited baseline distributions with available quantile functions.

Handles complex survival data structures like censoring, cure fractions, frailties, and competing risks.

Abstract

In this paper we propose a novel R package, called rsurv, developed for general survival data simulation purposes. The package is built under a new approach to simulate survival data that depends heavily on the use of dplyr verbs. The proposed package allows simulations of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package rsurv also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package rsurv lies in the fact that linear predictors are specified using R formulas, facilitating the inclusion of categorical variables, interaction terms and offset variables. The functions implemented in the package rsurv can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivarite survival data, and competing risks survival data.