Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models
纵向观察数据中的生存因果推断:序贯试验与边际结构模型
Longitudinal observational patient data can be used to investigate the causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for controlling for the time-dependent confounding that typically occurs. The most commonly used is inverse probability weighted estimation of marginal structural models (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, in particular in combination with the target…