Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study

Abstract

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.

Adjusting for Treatment Switching in Oncology Trials: A Systematic Review and Recommendations for Reporting

“…For this reason, other adjustment methods may be preferable in trials with high switching proportions. 1,10,12…”

“…Simulation studies have shown that these methods tend to produce more accurate estimates of the switching-adjusted estimand than simple adjustment methods or a standard intention to treat (ITT) analysis, but their performance can be compromised when underlying assumptions are violated. [10][11][12] Analyses using more rigorous adjustment methods are regularly submitted to HTA agencies, in some cases providing enough evidence to alter reimbursement decisions. In the case of sunitinib versus best supportive care for the treatment of gastrointestinal stromal tumors, for example, the incremental cost-effectiveness ratio per quality-adjusted life-year changed from £90500 in an ITT analysis to £31800 with the RSPFTM; this led to sunitinib being recommended for reimbursement by the National Institute for Health and Care Excellence (NICE).…”

Estimating efficacy in trials with selective crossover

“…It is also not possible to allow for time-dependent treatment effects. Other advantages and disadvantages of these methods have been discussed more fully elsewhere [18,19].…”

Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding

“…In common with previous simulation studies, 13,26 we simulated datasets with a sample size of 500 and 2:1 randomisation in favour of the experimental group, and with treatment switching permitted from the control group onto the experimental treatment. A step-by-step description of our data generating mechanism is provided in online Appendix A.…”