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<p>The AB/BA trial design is the simplest crossover design for comparing the effects of two treatments. It is widely applied in practice because it enables the direct comparison of two treatments for each individual while effectively controlling for inter-subject variability. However, unmeasured carryover effects can compromise the validity of the results, and this paper proposes the equivalence evaluation method for two treatment effects that account for carryover effects. A novel nonparametric statistical method is developed to quantify the treatment effect and the carryover effect, namely the win probability that a subject receiving one treatment or sequence achieves a better outcome (or “wins” against) compared to a subject receiving the other treatment or sequence. Five simultaneous confidence intervals for these two win probabilities are considered to evaluate the effectiveness of the two treatments, including those based on the normal distribution, <i>t</i> distribution, adjusted <i>t</i> distribution, logit transformation, and inverse-hyperbolic-sine transformation. Simulation results demonstrate that confidence interval procedures based on logit and inverse hyperbolic sine transformations perform well in terms of coverage and average interval width, even for small sample sizes, and hence are recommended for practical applications. Two AB/BA crossover trials involving continuous and ordinal outcomes are utilized to illustrate the proposed methods.</p>

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Rank-Based Nonparametric Methods for Evaluating Treatment Equivalence in AB/BA Crossover Trial Designs

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Shi-Fang Qiu,
  • Wang Yu,
  • Wai-Yin Poon

摘要

The AB/BA trial design is the simplest crossover design for comparing the effects of two treatments. It is widely applied in practice because it enables the direct comparison of two treatments for each individual while effectively controlling for inter-subject variability. However, unmeasured carryover effects can compromise the validity of the results, and this paper proposes the equivalence evaluation method for two treatment effects that account for carryover effects. A novel nonparametric statistical method is developed to quantify the treatment effect and the carryover effect, namely the win probability that a subject receiving one treatment or sequence achieves a better outcome (or “wins” against) compared to a subject receiving the other treatment or sequence. Five simultaneous confidence intervals for these two win probabilities are considered to evaluate the effectiveness of the two treatments, including those based on the normal distribution, t distribution, adjusted t distribution, logit transformation, and inverse-hyperbolic-sine transformation. Simulation results demonstrate that confidence interval procedures based on logit and inverse hyperbolic sine transformations perform well in terms of coverage and average interval width, even for small sample sizes, and hence are recommended for practical applications. Two AB/BA crossover trials involving continuous and ordinal outcomes are utilized to illustrate the proposed methods.