  A randomized trial allows estimation of the causal effect of an intervention
compared to a control in the overall population and in subpopulations defined
by baseline characteristics. Often, however, clinical questions also arise
regarding the treatment effect in subpopulations of patients, which would
experience clinical or disease related events post-randomization. Events that
occur after treatment initiation and potentially affect the interpretation or
the existence of the measurements are called {\it intercurrent events} in the
ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment,
randomization alone is no longer sufficient to meaningfully estimate the
treatment effect. Analyses comparing the subgroups of patients without the
intercurrent events for intervention and control will not estimate a causal
effect. This is well known, but post-hoc analyses of this kind are commonly
performed in drug development. An alternative approach is the principal stratum
strategy, which classifies subjects according to their potential occurrence of
an intercurrent event on both study arms. We illustrate with examples that
questions formulated through principal strata occur naturally in drug
development and argue that approaching these questions with the ICH E9(R1)
estimand framework has the potential to lead to more transparent assumptions as
well as more adequate analyses and conclusions. In addition, we provide an
overview of assumptions required for estimation of effects in principal strata.
Most of these assumptions are unverifiable and should hence be based on solid
scientific understanding. Sensitivity analyses are needed to assess robustness
of conclusions.
