Simulating longitudinal data from marginal structural models using the additive hazard model

• Published in: Biometrical journal Vol. 63; no. 7; pp. 1526 - 1541
• Main Authors: Keogh, Ruth H., Seaman, Shaun R., Gran, Jon Michael, Vansteelandt, Stijn
• Format: Journal Article
• Language: English
• Published: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.10.2021; Wiley - VCH Verlag GmbH
• Subjects: additive hazard model; Algorithms; causal inference; Computer Simulation; congenial models; Inference; longitudinal data; marginal structural model; Models, Statistical; Models, Structural; Proportional Hazards Models; Simulation; simulation study; Structural models; survival analysis; Time dependence; time‐dependent confounding; time-dependent confounding; survival analysis; additive hazard model; causal inference; marginal structural model; congenial models; simulation study; longitudinal data
• ISSN: 0323-3847; 1521-4036; 1521-4036
• DOI: 10.1002/bimj.202000040

Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time‐dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g‐formula, are popular for handling this problem. With increasing development of advanced causal inference methods, it is important to be able to assess their performance in different scenarios to guide their application. Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited. This paper focuses on the use of simulations for evaluations involving MSMs in studies with a time‐to‐event outcome. In a simulation, it is important to be able to generate the data in such a way that the correct forms of any models to be fitted to those data are known. However, this is not straightforward in the longitudinal setting because it is natural for data to be generated in a sequential conditional manner, whereas MSMs involve fitting marginal rather than conditional hazard models. We provide general results that enable the form of the correctly specified MSM to be derived based on a conditional data generating procedure, and show how the results can be applied when the conditional hazard model is an Aalen additive hazard or Cox model. Using conditional additive hazard models is advantageous because they imply additive MSMs that can be fitted using standard software. We describe and illustrate a simulation algorithm. Our results will help researchers to effectively evaluate causal inference methods via simulation.