  Treatment switching in a randomized controlled trial is said to occur when a
patient randomized to one treatment arm switches to another treatment arm
during follow-up. This can occur at the point of disease progression, whereby
patients in the control arm may be offered the experimental treatment. It is
widely known that failure to account for treatment switching can seriously
dilute the estimated effect of treatment on overall survival. In this paper, we
aim to account for the potential impact of treatment switching in a re-analysis
evaluating the treatment effect of NucleosideReverse Transcriptase Inhibitors
(NRTIs) on a safety outcome (time to first severe or worse sign or symptom) in
participants receiving a new antiretroviral regimen that either included or
omitted NRTIs in the Optimized Treatment That Includes or OmitsNRTIs (OPTIONS)
trial. We propose an estimator of a treatment causal effect under a structural
cumulative survival model (SCSM) that leverages randomization as an
instrumental variable to account for selective treatment switching. Unlike
Robins' accelerated failure time model often used to address treatment
switching, the proposed approach avoids the need for artificial censoring for
estimation. We establish that the proposed estimator is uniformly consistent
and asymptotically Gaussian under standard regularity conditions. A consistent
variance estimator is also given and a simple resampling approach provides
uniform confidence bands for the causal difference comparing treatment groups
overtime on the cumulative intensity scale. We develop an R package named
"ivsacim" implementing all proposed methods, freely available to download from
R CRAN. We examine the finite performance of the estimator via extensive
simulations.
