Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study. Latimer, N. R., Abrams, K. R., Lambert, P. C., Morden, J. P., & Crowther, M. J. Statistical Methods in Medical Research, 27(3):765–784, 2018. doi abstract bibtex 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.
@article{latimer_assessing_2018-1,
title = {Assessing methods for dealing with treatment switching in clinical trials: {A} follow-up simulation study},
volume = {27},
issn = {1477-0334},
shorttitle = {Assessing methods for dealing with treatment switching in clinical trials},
doi = {10.1177/0962280216642264},
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.},
language = {eng},
number = {3},
journal = {Statistical Methods in Medical Research},
author = {Latimer, N. R. and Abrams, K. R. and Lambert, P. C. and Morden, J. P. and Crowther, M. J.},
year = {2018},
pmid = {27114326},
keywords = {Biostatistics, Clinical Trial Protocols as Topic, Computer Simulation, Cross-Over Studies, Data Interpretation, Follow-Up Studies, health technology assessment, Humans, Kaplan-Meier Estimate, Models, oncology, overall survival, prediction, Proportional Hazards Models, Randomized Controlled Trials as Topic, Sample Size, Statistical, survival analysis, Survival Analysis, time-to-event outcomes, treatment crossover, Treatment switching},
pages = {765--784},
}
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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. 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