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Longitudinal observational patient data can be used to investigate the causaleffects of time-varying treatments on time-to-event outcomes. Several methodshave been developed for controlling for the time-dependent confounding thattypically occurs. The most commonly used is inverse probability weightedestimation of marginal structural models (MSM-IPTW). An alternative, thesequential trials approach, is increasingly popular, in particular incombination with the target trial emulation framework. This approach involvescreating a sequence of `trials' from new time origins, restricting toindividuals as yet untreated and meeting other eligibility criteria, andcomparing treatment initiators and non-initiators. Individuals are censoredwhen they deviate from their treatment status at the start of each `trial'(initiator/non-initiator) and this is addressed using inverse probability ofcensoring weights. The analysis is based on data combined across trials. Weshow that the sequential trials approach can estimate the parameter of aparticular MSM, and compare it to a MSM-IPTW with respect to the estimandsbeing identified, the assumptions needed and how data are used differently. Weshow how both approaches can estimate the same marginal risk differences. Thetwo approaches are compared using a simulation study. The sequential trialsapproach, which tends to involve less extreme weights than MSM-IPTW, results ingreater efficiency for estimating the marginal risk difference at mostfollow-up times, but this can, in certain scenarios, be reversed at late timepoints. We apply the methods to longitudinal observational data from the UKCystic Fibrosis Registry to estimate the effect of dornase alfa on survival.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models