  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 trial emulation framework. This approach involves
creating a sequence of `trials' from new time origins, restricting to
individuals as yet untreated and meeting other eligibility criteria, and
comparing treatment initiators and non-initiators. Individuals are censored
when they deviate from their treatment status at the start of each `trial'
(initiator/non-initiator) and this is addressed using inverse probability of
censoring weights. The analysis is based on data combined across trials. We
show that the sequential trials approach can estimate the parameter of a
particular MSM, and compare it to a MSM-IPTW with respect to the estimands
being identified, the assumptions needed and how data are used differently. We
show how both approaches can estimate the same marginal risk differences. The
two approaches are compared using a simulation study. The sequential trials
approach, which tends to involve less extreme weights than MSM-IPTW, results in
greater efficiency for estimating the marginal risk difference at most
follow-up times, but this can, in certain scenarios, be reversed at late time
points. We apply the methods to longitudinal observational data from the UK
Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.
