title: Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.
abstract: 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.
authors.affiliation: 1 School of Health and Related Research, University of Sheffield, Sheffield, UK.
authors.forename: Nicholas R
authors.initials: NR
authors.lastname: Latimer
authors.affiliation: 2 Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: K R
authors.initials: KR
authors.lastname: Abrams
authors.affiliation: 2 Department of Health Sciences, University of Leicester, Leicester, UK.|3 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: P C
authors.initials: PC
authors.lastname: Lambert
authors.affiliation: 2 Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: M J
authors.initials: MJ
authors.lastname: Crowther
authors.affiliation: 1 School of Health and Related Research, University of Sheffield, Sheffield, UK.
authors.forename: A J
authors.initials: AJ
authors.lastname: Wailoo
authors.affiliation: 4 Clinical Trials and Statistics Unit (ICR-CTSU), Division of Clinical Studies, The Institute of Cancer Research, London, UK.
authors.forename: J P
authors.initials: JP
authors.lastname: Morden
authors.affiliation: 1 School of Health and Related Research, University of Sheffield, Sheffield, UK.
authors.forename: R L
authors.initials: RL
authors.lastname: Akehurst
authors.affiliation: 1 School of Health and Related Research, University of Sheffield, Sheffield, UK.
authors.forename: M J
authors.initials: MJ
authors.lastname: Campbell
mesh_terms: D000465:Algorithms; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D018592:Cross-Over Studies; D018450:Disease Progression; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016019:Survival Analysis; D013673:Technology Assessment, Biomedical / Q000706:statistics & numerical data
keywords: health technology assessment; inverse probability of censoring weights; prediction; survival analysis; time-to-event outcomes; treatment crossover; treatment switching