Simulating data from marginal structural models for a survival time outcome

TL;DR

This paper introduces a new simulation method for marginal structural models (MSMs) that overcomes previous restrictions, enabling more flexible evaluation of causal inference methods for survival outcomes in observational studies.

Contribution

The authors propose a novel simulation algorithm for MSMs that accommodates various types of survival data, covariates, and treatment variables, improving upon existing methods.

Findings

The new simulation method is flexible for different MSM types and data scenarios.

Simulation results show improved coverage of confidence intervals for causal estimates.

The method facilitates more accurate evaluation of causal inference techniques in survival analysis.

Abstract

Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, e.g., inverse probability of treatment weighting (IPTW). It is important to evaluate the performance of statistical methods in different scenarios, and simulation studies are a key tool for such evaluations. In such simulation studies, it is common to generate data in such a way that the model of interest is correctly specified, but this is not always straightforward when the model of interest is for potential outcomes, as is an MSM. Methods have been proposed for simulating from MSMs for a survival outcome, but these methods impose restrictions on the data-generating mechanism. Here we propose a method that overcomes these restrictions. The MSM can be a marginal structural logistic model for a discrete survival time or a Cox or additive hazards MSM for a continuous survival time. The hazard of the potential survival time can be conditional on baseline covariates, and the treatment variable can be discrete or continuous. We illustrate the use of the proposed simulation algorithm by carrying out a brief simulation study. This study compares the coverage of confidence intervals calculated in two different ways for causal effect estimates obtained by fitting an MSM via IPTW.