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Clinical trials often allow patients in the control arm to switch to thetreatment arm if their physical conditions are worse than certain tolerancelevels. For instance, treatment switching arises in the Concorde clinicaltrial, which aims to assess causal effects on the time-to-disease progressionor death of immediate versus deferred treatment with zidovudine among patientswith asymptomatic HIV infection. The Intention-To-Treat analysis does notmeasure the effect of the actual receipt of the treatment and ignores theinformation on treatment switching. Other existing methods reconstruct theoutcome a patient would have had if they had not switched under strongassumptions. Departing from the literature, we re-define the problem oftreatment switching using principal stratification and focus on causal effectsfor patients belonging to subpopulations defined by the switching behaviorunder control. We use a Bayesian approach to inference, taking into accountthat (i) switching happens in continuous time; (ii) switching time is notdefined for patients who never switch in a particular experiment; and (iii)survival time and switching time are subject to censoring. We apply thisframework to analyze synthetic data based on the Concorde study. Our dataanalysis reveals that immediate treatment with zidovudine increases survivaltime for never switcher and that treatment effects are highly heterogeneousacross different types of patients defined by the switching behavior.

Assessing causal effects in the presence of treatment switching through principal stratification