Institute of Medical Biostatistics, Epidemiology and Informatics, University Hospital Mainz, Mainz, Germany.
Dtsch Arztebl Int. 2012 Apr;109(15):276-81. doi: 10.3238/arztebl.2012.0276. Epub 2012 Apr 13.
Many clinical trials have a crossover design. Certain considerations that are relevant to the crossover design, but play no role in standard parallel-group trials, must receive adequate attention in trial planning and data analysis for the results to be of scientific value.
The authors present the basic statistical methods required for the analysis of crossover trials, referring to standard statistical texts.
In the simplest and most common scenario, a crossover trial involves two treatments which are consecutively administered in each patient recruited in the study. The main purpose served by the design is to provide a basis for separating treatment effects from period effects. This is achieved via computing the treatment effects separately in two sequence groups formed via randomization. The differences between treatment effects can be assessed by means of a standard t-test for independent samples using the intra-individual differences between the outcomes in both periods as the raw data. The existence of carryover effects must be ruled out for this method to be valid. This assumption is usually checked using a pre-test, which is also described in this article. Finally, we briefly discuss the use of nonparametric tests instead of t-tests and more complicated designs with more than two test periods and/or treatments.
Crossover trials in which the results are not analyzed separately by sequence group are of limited, if any, scientific value. It is also essential to guard against carryover effects. Whenever ignoring such effects proves unjustified, the treatment effect must be analyzed solely via an analysis of the data obtained during the first trial period. Even the use of this restricted dataset yields results whose validity is not beyond question.
许多临床试验采用交叉设计。某些与交叉设计相关但在标准平行组试验中不起作用的考虑因素，必须在试验计划和数据分析中得到充分关注，才能使结果具有科学价值。
作者参考标准统计文本，介绍了分析交叉试验所需的基本统计方法。
在最简单和最常见的情况下，交叉试验涉及两种治疗方法，这些治疗方法在研究中招募的每个患者中依次进行。该设计的主要目的是提供一种基础，将治疗效果与周期效果分开。这是通过在通过随机化形成的两个序列组中分别计算治疗效果来实现的。可以使用标准的独立样本 t 检验，使用两个周期内结果之间的个体内差异作为原始数据，来评估治疗效果之间的差异。这种方法有效，必须排除残留效应。通常使用预测试来检查此假设，本文也对此进行了描述。最后，我们简要讨论了使用非参数检验代替 t 检验，以及具有两个以上测试周期和/或治疗方法的更复杂设计。
如果不按顺序组分别分析结果，则交叉试验的科学价值有限。防范残留效应也很重要。只要证明忽略这些影响是不合理的，就必须仅通过分析第一个试验周期获得的数据来分析治疗效果。即使使用这种受限数据集，也会得出其有效性并非无懈可击的结果。
