Adjusting for treatment switching in randomised controlled trials – A simulation study and a simplified two-stage method

• Published in: Statistical methods in medical research 2017-04, Vol.26 (2), p.724-751
• Main Authors: Latimer, Nicholas R, Abrams, KR, Lambert, PC, Crowther, MJ, Wailoo, AJ, Morden, JP, Akehurst, RL, Campbell, MJ
• Format: Article
• Language: English
• Subjects: Adjustment; Algorithms; Bias; Biostatistics - methods; Cancer; Clinical trials; Computer Simulation; Cross-Over Studies; Dependency; Disease Progression; Estimates; Failure times; Humans; Iterative methods; Medical prognosis; Medical technology; Medicin och hälsovetenskap; Methods; Models, Statistical; Parameter estimation; Production methods; Randomized Controlled Trials as Topic - statistics & numerical data; Severity; Simulation; Structural failure; Survival; Survival Analysis; Switching; Technology assessment; Technology Assessment, Biomedical - statistics & numerical data; Time dependence; Treatment needs
• ISSN: 0962-2802; 1477-0334; 1477-0334
• DOI: 10.1177/0962280214557578

Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) – whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.