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.Comment: 29 pages, 2 figure