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.

翻译：边际结构模型（MSMs）常用于从观察性数据中估计治疗对生存时间结局的因果效应，尤其是当可能存在时依性混杂时。此类模型可通过逆概率治疗加权（IPTW）等方法进行拟合。在不同场景下评估统计方法的性能至关重要，而模拟研究是实现此类评估的关键工具。在模拟研究中，通常需确保目标模型被正确设定，但当目标模型针对潜在结局（如MSM）时，这一过程并不简单。现有方法虽已提出针对生存结局的MSM数据模拟，但这些方法对数据生成机制施加了限制。本文提出一种突破这些限制的新方法。该MSM可以是离散生存时间的边际结构逻辑模型，或连续生存时间的Cox比例风险或加性风险MSM。潜在生存时间的风险可依赖基线协变量，治疗变量可为离散或连续型。我们通过一项简要的模拟研究展示了所提模拟算法的应用，该研究比较了两种不同置信区间计算方式对通过IPTW拟合MSM所获因果效应估计值的覆盖率。