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Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study.

By James P. Morden, Paul C. Lambert, Nicholas Latimer, Keith R. Abrams and Allan J. Wailoo


Background: We investigate methods used to analyse the results of clinical trials with survival outcomes in which\ud some patients switch from their allocated treatment to another trial treatment. These included simple methods\ud which are commonly used in medical literature and may be subject to selection bias if patients switching are not\ud typical of the population as a whole. Methods which attempt to adjust the estimated treatment effect, either\ud through adjustment to the hazard ratio or via accelerated failure time models, were also considered. A\ud simulation study was conducted to assess the performance of each method in a number of different scenarios.\ud Results: 16 different scenarios were identified which differed by the proportion of patients switching, underlying\ud prognosis of switchers and the size of true treatment effect. 1000 datasets were simulated for each of these and\ud all methods applied. Selection bias was observed in simple methods when the difference in survival between\ud switchers and non-switchers were large. A number of methods, particularly the AFT method of Branson and\ud Whitehead were found to give less biased estimates of the true treatment effect in these situations.\ud Conclusions: Simple methods are often not appropriate to deal with treatment switching. Alternative\ud approaches such as the Branson & Whitehead method to adjust for switching should be considered.Peer reviewedPublisher Versio

Publisher: BioMed Central Ltd
Year: 2011
DOI identifier: 10.1186/1471-2288-11-4
OAI identifier:

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  5. (2004). AG: Parametric randomization-based methods for correcting for treatment changes in the assessment of the causal effect of treatment. Statistics in Medicine doi
  6. (1993). Analysis as-randomized and the problem of nonadherence - An example from the Veterans Affairs randomized trial of coronary-artery bypass-surgery. Statistics in Medicine doi
  7. (1991). Analysis of clinical trials by treatment actually received: is it really an option? Stat Med doi
  8. (2002). Babiker A: strbee: Randomization-based efficacy estimator.
  9. (1989). Bivariate survival models induced by frailties. doi
  10. (2006). Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting. Basic and Clinical Pharmacology and Toxicology doi
  11. (1999). De Stavola B: Cancer survival trends in England and Wales, 1971-1995: deprivation and NHS region Office of National Statistics;
  12. (2002). Estimating a treatment effect in survival studies in which patients switch treatment. Statistics in Medicine doi
  13. (2005). Gemcitabine for the treatment of metastatic breast cancer, doi
  14. (2005). Generating survival times to simulate Cox proportional hazards models. Statistics in Medicine doi
  15. (2003). Goetghebeur E: A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-ornothing compliance. Biometrics doi
  16. (1997). JH: Impact of treatment changes on the interpretation of the Concorde trial. Aids doi
  17. (1996). Kaldor JM: Survival analyses of randomized clinical trials adjusted for patients who switch treatments. Statistics in Medicine doi
  18. Letter to the editor: Estimating treatment effects in randomized trials with treatment switching. doi
  19. (1997). Letters to the editor: Survival analyses of randomized clinical trials adjusted for patients who switch treatments. Statistics in Medicine doi
  20. (2002). Loeys T: Beyond intention to treat. Epidemiologic Reviews doi
  21. (2010). McNamee R: Performance of statistical methods for analysing survival data in the presence of non-random compliance. Statistics in Medicine doi
  22. (2002). Model misspecification sensitivity analysis in estimating causal effects of interventions with non-compliance. Statistics in Medicine doi
  23. Modelling survival data in medical research Chapman and Hall; doi
  24. (1998). More powerful randomization-based p-values in double-blind trials with non-compliance. Statistics in Medicine doi
  25. (2003). on the use of capecitabine for the treatment of locally advanced or metastatic breast cancer,
  26. (2005). Oxaliplatin and Raltitrexed for Advanced Colorectal Cancer (review of TA33).
  27. (1996). Pocock SJ: Statistical reporting of clinical trials with individual changes from allocated treatment. Statistics in Medicine doi
  28. (2002). Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis. BMJ doi
  29. (2002). Principal Stratification in Causal Inference. Biometrics doi
  30. (1999). Randomization-based methods for correcting for treatment changes: Examples from the Concorde trial. Statistics in Medicine doi
  31. (2006). RL: The design of simulation studies in medical statistics. Statistics in Medicine doi
  32. (2006). S: Methodological issues in the economic analysis of cancer treatments. doi
  33. (2009). sorafenib (first- and second-line), sunitinib (second-line) and temsirolimus (first-line) for the treatment of advanced and/or metastatic renal cell carcinoma. doi
  34. (2009). Stata software
  35. (2005). Statistical inference for cancer trials with treatment switching. Statistics in Medicine doi
  36. (2003). Survival analysis part II: Mulitvariate data analysis - an introduction to concepts and methods. doi
  37. (2002). The clinical effectiveness and cost effectiveness of trastuzumab for breast cancer. doi
  38. (1993). Tibshirini R: An introduction to the bootstrap Monographs on Statistics and Applied Probability. Chapman and Hall London;
  39. (1991). Tsiatis AA: Correcting for non-compliance in randomized trials using rank preserving structural failure time models. doi
  40. (2005). Uses and limitations of randomization-based efficacy estimators. doi
  41. (2008). Wailoo A: Bevacizumab, sorafenib, sunitinib and temsirolimus for renal cell carcinoma. Decision Support Unit Report
  42. (2004). White IR: Compliance-adjusted intervention effects in survival data.

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