  In clinical trials, patients sometimes discontinue study treatments
prematurely due to reasons such as adverse events. Treatment discontinuation
occurs after the randomisation as an intercurrent event, making causal
inference more challenging. The Intention-To-Treat (ITT) analysis provides
valid causal estimates of the effect of treatment assignment; still, it does
not take into account whether or not patients had to discontinue the treatment
prematurely. We propose to deal with the problem of treatment discontinuation
using principal stratification, recognised in the ICH E9(R1) addendum as a
strategy for handling intercurrent events. Under this approach, we can
decompose the overall ITT effect into principal causal effects for groups of
patients defined by their potential discontinuation behaviour in continuous
time. In this framework, we must consider that discontinuation happening in
continuous time generates an infinite number of principal strata and that
discontinuation time is not defined for patients who would never discontinue.
An additional complication is that discontinuation time and time-to-event
outcomes are subject to administrative censoring. We employ a flexible
model-based Bayesian approach to deal with such complications. We apply the
Bayesian principal stratification framework to analyse synthetic data based on
a recent RCT in Oncology, aiming to assess the causal effects of a new
investigational drug combined with standard of care vs. standard of care alone
on progression-free survival. We simulate data under different assumptions that
reflect real situations where patients' behaviour depends on critical baseline
covariates. Finally, we highlight how such an approach makes it straightforward
to characterise patients' discontinuation behaviour with respect to the
available covariates with the help of a simulation study.
