A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model

Abstract

AbstractBackgroundTreatment 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 Nucleoside Reverse 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 Omits NRTIs (OPTIONS) trial.MethodsWe 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 over time 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 estimator via extensive simulations.Results357 participants in the OPTIONS trial were randomly assigned at baseline to add-NRTIs or omit-NRTIs treatment group; 93% subsequently completed a 48-week visit. Using the proposed methods, we found statistically significant evidence against the sharp null hypothesis of no treatment effect on the safety outcome (P value 0.034) and our SCSM estimator revealed an increased risk for a safety outcome in participants receiving a new antiretroviral regimen that included NRTIs when compared to participants receiving a regimen that omitted NRTIs. In fact, under an SCSM encoding a constant additive hazards model, we estimated a hazards difference equal to 0.0039 (95% CI 0.0002, 0.0075) over the 48-week follow-up.ConclusionsTreatment-experienced patients with HIV infection starting a new optimized regimen will experience a higher risk of severe or worse sign or symptom. Previous analyses concluded that treatment-experienced patients with HIV infection starting a new optimized regimen can safely omit NRTIs without compromising virologic efficacy. Our analysis suggests that adding NRTIs is not only unnecessary to achieve optimal outcomes but may increase the risk for a safety outcome.