Inference of median difference based on the Box–Cox model in randomized clinical trials
Inference of median difference based on the Box–Cox model in randomized clinical trials
复制标题
基于Box-Cox模型的随机临床试验中位数差异的推断
DOI：
10.1002/sim.6408
复制
发表时间：
2015
影响因子：
2
通讯作者：
Masahiko Gosho
中科院分区：
文献类型：
--
作者：
K. Maruo;Naoki Isogawa;Masahiko Gosho
在随机临床试验中，许多医学和生物测量数据不是正态分布的，往往是不对称的。Box-Cox变换是在统计检验方面比较偏斜连续变量的两个处理组的一个强有力的程序。然而，在原有的测量尺度上，很难直接估计和解释两组之间的位置差异。我们提出了一种有用的方法，以更容易解释的形式推断治疗效果在原始量表上的差异。我们还提供统计分析包，包括对治疗效果的估计、协方差调整、标准误差和统计假设检验。对两个处理组的随机平行分组临床试验的仿真研究表明，在I类错误率和功率方面，该方法的性能与现有的非参数方法相当或更好。在一个获得性免疫缺陷综合征的临床试验中，我们用分类4数据来说明我们的方法。版权所有©2015 John Wiley&Sons，Ltd.
In randomized clinical trials, many medical and biological measurements are not normally distributed and are often skewed. The Box–Cox transformation is a powerful procedure for comparing two treatment groups for skewed continuous variables in terms of a statistical test. However, it is difficult to directly estimate and interpret the location difference between the two groups on the original scale of the measurement. We propose a helpful method that infers the difference of the treatment effect on the original scale in a more easily interpretable form. We also provide statistical analysis packages that consistently include an estimate of the treatment effect, covariance adjustments, standard errors, and statistical hypothesis tests. The simulation study that focuses on randomized parallel group clinical trials with two treatment groups indicates that the performance of the proposed method is equivalent to or better than that of the existing non‐parametric approaches in terms of the type‐I error rate and power. We illustrate our method with cluster of differentiation 4 data in an acquired immune deficiency syndrome clinical trial. Copyright © 2015 John Wiley & Sons, Ltd.