Assessing Causal Effects in the Presence of Treatment Switching Through Principal Stratification

Abstract

Clinical trials often allow patients in the control arm to switch to the treatment arm if their physical conditions are worse than certain tolerance levels. For instance, treatment switching arises in the Concorde clinical trial, which aims to assess causal effects on the time-to-disease progression or death of immediate versus deferred treatment with zidovudine among patients with asymptomatic HIV infection. The Intention-To-Treat analysis does not measure the effect of the actual receipt of the treatment and ignores the information on treatment switching. Other existing methods reconstruct the outcome a patient would have had if they had not switched under strong assumptions. Departing from the literature, we re-define the problem of treatment switching using principal stratification and focus on causal effects for patients belonging to subpopulations defined by the switching behavior under control. We use a Bayesian approach to inference, taking into account that $(i)$ switching happens in continuous time; $(ii)$ switching time is not defined for patients who never switch in a particular experiment; and $(iii)$ survival time and switching time are subject to censoring. We apply this framework to analyze the synthetic data based on the Concorde study.