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In a randomized controlled trial, treatment switching (also calledcontamination or crossover) occurs when a patient initially assigned to onetreatment arm changes to another arm during the course of follow-up.Overlooking treatment switching might substantially bias the evaluation oftreatment efficacy or safety. To account for treatment switching, instrumentalvariable (IV) methods by leveraging the initial randomized assignment as an IVserve as natural adjustment methods because they allow dependent treatmentswitching possibly due to underlying prognoses. However, the ``exclusionrestriction'' assumption for IV methods, which requires the initialrandomization to have no direct effect on the outcome, remains questionable,especially for open-label trials. We propose a robust instrumental variableestimator circumventing such a caveat. We derive large-sample properties of ourproposed estimator, along with inferential tools. We conduct extensivesimulations to examine the finite performance of our estimator and itsassociated inferential tools. An R package ``ivsacim'' implementing allproposed methods is freely available on R CRAN. We apply the estimator toevaluate the treatment effect of Nucleoside Reverse Transcriptase Inhibitors(NRTIs) on a safety outcome in the Optimized Treatment That Includes or OmitsNRTIs trial.

A Robust Instrumental Variable Method Accounting for Treatment Switching in Open-Label Randomized Controlled Trials