Simulating from marginal structural models for hazards, cause-specific hazards and subdistribution hazards using general copulas

TL;DR

This paper extends a method for simulating survival data under marginal structural models by allowing non-Gaussian copulas and accommodating cause-specific and subdistribution hazards with competing risks.

Contribution

It introduces two extensions to existing simulation methods: using non-Gaussian copulas while preserving interpretability and simulating data for cause-specific and subdistribution hazards.

Findings

Method supports non-Gaussian copulas with interpretability.

Enables simulation of competing risks with cause-specific and subdistribution hazards.

Broadens the applicability of simulation techniques for survival analysis.

Abstract

Seaman and Keogh (Biometrical Journal 2024) proposed a method for simulating data compatible with a marginal structural model (MSM) for the hazard of a survival time outcome. In this short report, I propose two extensions of this method. First, Seaman and Keogh favoured the use of a Gaussian copula, because this enables the function of the confounder history through which the hazard of failure depends on confounders to be interpreted as a risk score. Here, I describe how this interpretation can be preserved even when a non-Gaussian copula is used. Second, I extend Seaman and Keogh's method to allow simulation of data compatible with a MSM for a cause-specific or subdistribution hazard of failure in the presence of a competing event.