Statistical Analysis of Noninferiority Trials with a Rate Ratio in Small‐Sample Matched‐Pair Designs
Statistical Analysis of Noninferiority Trials with a Rate Ratio in Small‐Sample Matched‐Pair Designs
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DOI：
10.1111/j.0006-341x.2003.00134.x
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发表时间：
2003-12
期刊：
影响因子：
1.9
通讯作者：
I. S. Chan;Niansheng Tang;M. Tang;P. Chan
中科院分区：
文献类型：
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作者：
I. S. Chan;Niansheng Tang;M. Tang;P. Chan
摘要非劣效性检验在现代医学中作为一种比较新的检验方法与现有检验方法的手段变得越来越重要。最近开发了渐近方法，用于在配对设计下使用率比分析非劣效性试验。然而，在小样本中，这些渐近方法的性能可能不可靠，因此不推荐使用。在这篇文章中，我们研究了评估非劣效性试验的替代方法，在小样本配对设计下使用率比测量。特别是，我们提出了一个精确和近似精确的无条件测试，沿着相应的置信区间的基础上的得分统计。精确无条件方法保证I类错误率不会超过标称水平。建议在需要严格控制I类错误（防止接受劣质治疗的任何夸大风险）时使用。然而，精确的方法往往过于保守（因此，不太强大）和计算要求。通过实证研究，我们表明，近似精确的分数方法，这是计算简单的实现，控制I型错误率相当不错，并具有较高的功率假设检验。总的来说，近似精确方法为分析来自小样本量配对设计的相关二进制数据提供了一个非常好的选择。我们用两个真实的例子说明这些方法，这些例子取自软镜片的交叉研究和卡氏肺孢子虫肺炎研究。我们对比的方法与一个假设的例子。
Summary. Testing of noninferiority has become increasingly important in modern medicine as a means of comparing a new test procedure to a currently available test procedure. Asymptotic methods have recently been developed for analyzing noninferiority trials using rate ratios under the matched‐pair design. In small samples, however, the performance of these asymptotic methods may not be reliable, and they are not recommended. In this article, we investigate alternative methods that are desirable for assessing noninferiority trials, using the rate ratio measure under small‐sample matched‐pair designs. In particular, we propose an exact and an approximate exact unconditional test, along with the corresponding confidence intervals based on the score statistic. The exact unconditional method guarantees the type I error rate will not exceed the nominal level. It is recommended for when strict control of type I error (protection against any inflated risk of accepting inferior treatments) is required. However, the exact method tends to be overly conservative (thus, less powerful) and computationally demanding. Via empirical studies, we demonstrate that the approximate exact score method, which is computationally simple to implement, controls the type I error rate reasonably well and has high power for hypothesis testing. On balance, the approximate exact method offers a very good alternative for analyzing correlated binary data from matched‐pair designs with small sample sizes. We illustrate these methods using two real examples taken from a crossover study of soft lenses and a Pneumocystis carinii pneumonia study. We contrast the methods with a hypothetical example.