  Intercurrent (post-treatment) events occur frequently in randomized trials,
and investigators often express interest in treatment effects that suitably
take account of these events. A naive conditioning on intercurrent events does
not have a straight-forward causal interpretation, and the practical relevance
of other commonly used approaches is debated. In this work, we discuss how to
formulate and choose an estimand, beyond the marginal intention to treat
effect, from the point of view of a decision maker and drug developer. In
particular, we argue that careful articulation of a practically useful research
question should either reflect decision making at this point in time or future
drug development. Indeed, a substantially interesting estimand is a
formalization of the (plain English) description of a research question. A
common feature of estimands that are practically useful is that they correspond
to possibly hypothetical but well-defined interventions in identifiable
(sub)populations. To illustrate our points, we consider five examples that were
recently used to motivate consideration of principal stratum estimands in
clinical trials. In all of these examples, we propose alternative causal
estimands, such as conditional effects, sequential regime effects and separable
effects, that correspond to explicit research questions of substantial
interest. Certain questions require stronger assumptions for identification.
However, we highlight that our proposed estimands require less stringent
assumptions than estimands commonly targeted in these settings, including
principal stratum effects.
