title: Treatment crossovers in time-to-event non-inferiority randomised trials of radiotherapy in patients with breast cancer.
abstract: BACKGROUND
  In non-inferiority trials of radiotherapy in patients with early stage breast cancer, it is inevitable that some patients will cross over from the experimental arm to the standard arm prior to initiation of any treatment due to complexities in treatment planning or subject preference. Although the intention-to-treat (ITT) analysis is the preferred approach for superiority trials, its role in non-inferiority trials is still under debate. This has led to the use of alternative approaches such as the per-protocol (PP) analysis or the as-treated (AT) analysis, despite the inherent biases of such approaches.
  
  
  METHODS
  Using simulations, we investigate the effect of 2%, 5% and 10% random and non-random crossovers prior to radiotherapy initiation on the ITT, PP, AT and the combination of ITT and PP analyses with respect to type I error in trials with time-to-event outcomes. We also evaluate bias and SE of the estimates from the ITT, PP and AT approaches.
  
  
  RESULTS
  The AT approach had the best performance in terms of type I error, but was anticonservative as non-random crossover increased. The ITT and PP approaches were anticonservative under all percentages of random and non-random crossover. Similarly, lowest bias was seen with the AT approach; however, bias increased as the percentage of non-random crossover increased. The ITT and PP had poor performance in terms of bias as crossovers increased.
  
  
  CONCLUSIONS
  If minimal crossovers were to occur, we have shown that the AT approach has the lowest type I error rates and smallest opportunity for bias. Results of trials with a high number of crossovers should be interpreted with caution, especially when crossover is non-random. Attempts to prevent crossovers should be maximised.
authors.affiliation: Ontario Clinical Oncology Group, Department of Oncology, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Sameer
authors.initials: S
authors.lastname: Parpia
authors.affiliation: Ontario Clinical Oncology Group, Department of Oncology, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Jim A
authors.initials: JA
authors.lastname: Julian
authors.affiliation: Centre of Evaluation of Medicines, St Joseph's Hospital, Hamilton, Ontario, Canada.|Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Lehana
authors.initials: L
authors.lastname: Thabane
authors.affiliation: Ontario Clinical Oncology Group, Department of Oncology, McMaster University, Hamilton, Ontario, Canada.
authors.forename: Chushu
authors.initials: C
authors.lastname: Gu
authors.affiliation: Ontario Clinical Oncology Group, Department of Oncology, McMaster University, Hamilton, Ontario, Canada.|Juravinski Cancer Centre, Hamilton, Ontario, Canada.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Whelan
authors.affiliation: Ontario Clinical Oncology Group, Department of Oncology, McMaster University, Hamilton, Ontario, Canada.|Juravinski Cancer Centre, Hamilton, Ontario, Canada.
authors.forename: Mark N
authors.initials: MN
authors.lastname: Levine
mesh_terms: D015982:Bias; D001943:Breast Neoplasms / Q000532:radiotherapy*; D003198:Computer Simulation; D018592:Cross-Over Studies; D005260:Female; D006801:Humans; D057194:Intention to Treat Analysis; D016032:Randomized Controlled Trials as Topic; D013223:Statistics as Topic; D016896:Treatment Outcome
keywords: STATISTICS & RESEARCH METHODS