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 ha...