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In clinical trials, patients sometimes discontinue study treatmentsprematurely due to reasons such as adverse events. Treatment discontinuationoccurs after the randomisation as an intercurrent event, making causalinference more challenging. The Intention-To-Treat (ITT) analysis providesvalid causal estimates of the effect of treatment assignment; still, it doesnot take into account whether or not patients had to discontinue the treatmentprematurely. We propose to deal with the problem of treatment discontinuationusing principal stratification, recognised in the ICH E9(R1) addendum as astrategy for handling intercurrent events. Under this approach, we candecompose the overall ITT effect into principal causal effects for groups ofpatients defined by their potential discontinuation behaviour in continuoustime. In this framework, we must consider that discontinuation happening incontinuous time generates an infinite number of principal strata and thatdiscontinuation time is not defined for patients who would never discontinue.An additional complication is that discontinuation time and time-to-eventoutcomes are subject to administrative censoring. We employ a flexiblemodel-based Bayesian approach to deal with such complications. We apply theBayesian principal stratification framework to analyse synthetic data based ona recent RCT in Oncology, aiming to assess the causal effects of a newinvestigational drug combined with standard of care vs. standard of care aloneon progression-free survival. We simulate data under different assumptions thatreflect real situations where patients' behaviour depends on critical baselinecovariates. Finally, we highlight how such an approach makes it straightforwardto characterise patients' discontinuation behaviour with respect to theavailable covariates with the help of a simulation study.

Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation due to adverse events