  The treatment effect in a specific subgroup is often of interest in
randomized clinical trials. When the subgroup is characterized by the absence
of certain post-randomization events, a naive analysis on the subset of
patients without these events may be misleading. The principal stratification
framework allows one to define an appropriate causal estimand in such settings.
Statistical inference for the principal stratum estimand hinges on
scientifically justified assumptions, which can be included with Bayesian
methods through prior distributions. Our motivating example is a large
randomized placebo-controlled trial of siponimod in patients with secondary
progressive multiple sclerosis. The primary objective of this trial was to
demonstrate the efficacy of siponimod relative to placebo in delaying
disability progression for the whole study population. However, the treatment
effect in the subgroup of patients who would not relapse during the trial is
relevant from both a scientific and regulatory perspective. Assessing this
subgroup treatment effect is challenging as there is strong evidence that
siponimod reduces relapses. Aligned with the draft regulatory guidance ICH
E9(R1), we describe in detail the scientific question of interest, the
principal stratum estimand, the corresponding analysis method for binary
endpoints and sensitivity analyses.
