  Intensity-based multistate models provide a useful framework for
characterizing disease processes, the introduction of interventions, loss to
follow-up, and other complications arising in the conduct of randomized trials
studying complex life history processes. Within this framework we discuss the
issues involved in the specification of estimands and show the limiting values
of common estimators of marginal process features based on cumulative incidence
function regression models. When intercurrent events arise we stress the need
to carefully define the target estimand and the importance of avoiding targets
of inference that are not interpretable in the real world. This has
implications for analyses, but also the design of clinical trials where
protocols may help in the interpretation of estimands based on marginal
features.
