Lui Kung-Jong, Chang Kuang-Chao
Department of Mathematics and Statistics, College of Sciences, San Diego State University, San Diego, CA, USA
Department of Statistics and Information Science, Fu-Jen Catholic University, New Taipei, Taiwan.
Stat Methods Med Res. 2016 Feb;25(1):385-99. doi: 10.1177/0962280212455753. Epub 2012 Aug 16.
When the frequency of occurrence for an event of interest follows a Poisson distribution, we develop asymptotic and exact procedures for testing non-equality, non-inferiority and equivalence, as well as asymptotic and exact interval estimators for the ratio of mean frequencies between two treatments under a simple crossover design. Using Monte Carlo simulations, we evaluate the performance of these test procedures and interval estimators in a variety of situations. We note that all asymptotic test procedures developed here can generally perform well with respect to Type I error and can be preferable to the exact test procedure with respect to power if the number of patients per group is moderate or large. We further find that in these cases the asymptotic interval estimator with the logarithmic transformation can be more precise than the exact interval estimator without sacrificing the accuracy with respect to the coverage probability. However, the exact test procedure and exact interval estimator can be of use when the number of patients per group is small. We use a double-blind randomized crossover trial comparing salmeterol with a placebo in exacerbations of asthma to illustrate the practical use of these estimators.
当感兴趣事件的发生频率服从泊松分布时，我们针对简单交叉设计下两种治疗方法的平均频率之比，开发了用于检验非相等性、非劣效性和等效性的渐近和精确程序，以及渐近和精确区间估计量。通过蒙特卡罗模拟，我们评估了这些检验程序和区间估计量在各种情况下的性能。我们注意到，这里开发的所有渐近检验程序在I型错误方面通常表现良好，并且如果每组患者数量适中或较大，在检验效能方面可能优于精确检验程序。我们进一步发现，在这些情况下，采用对数变换的渐近区间估计量在不牺牲覆盖概率准确性的前提下，可能比精确区间估计量更精确。然而，当每组患者数量较少时，精确检验程序和精确区间估计量可能会有用。我们使用一项比较沙美特罗与安慰剂治疗哮喘急性加重的双盲随机交叉试验来说明这些估计量的实际应用。
