Chis Ster Anca, Phillips Rachel, Sauzet Odile, Cornelius Victoria
Imperial Clinical Trials Unit, School of Public Health, Imperial College London, 1st Floor Stadium House, 68 Wood Lane, London, W12 7RH, UK.
Bielefeld School of Public health, Bielefeld University, Universitätstr. 25, 33 615, Bielefeld, Germany.
Trials. 2021 Jun 29;22(1):419. doi: 10.1186/s13063-021-05343-0.
Randomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means.
We explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect 'signals' for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared.
Within trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher's exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium).
When dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation.
随机对照试验（RCT）为制定危害概况提供了有价值的信息，但目前用于检测组间差异的分析方法并不理想。药物试验通常会筛选连续的临床和生物学数据以监测参与者的危害。这些结果通常会被二分法分为异常/正常数值进行分析。尽管这为临床解释带来了简便性，但众所周知，将结果二分法会导致信息大幅减少，从而降低统计功效。我们提出了一种自动化程序，用于常规实施由皮科克（Peacock）和绍泽特（Sauzet）提出的连续结果二分法的分布方法，该方法保留了均值比较的精度。
我们探索了使用一种分布方法，基于均值比较来比较比例差异，该方法保留了均值比较的功效。我们将这种方法应用于临床和生物学数据的筛选，作为检测潜在药物不良反应（ADR）“信号”的一种手段。然后可以在进一步的验证性研究中对信号进行跟进。描述了三种适用于不同类型分布的分布方法。我们建议使用一种自动化方法，利用观察到的数据在RCT环境中针对多个连续结果选择最合适的分布作为分析策略。我们使用来自三项评估美泊利珠单抗治疗哮喘或慢性阻塞性肺疾病（COPD）疗效的RCT数据来说明这种方法。已发表的参考范围用于定义10项血液检测子集中异常值参与者的比例。对每项血液检测估计组间分布和比例的经验差异并进行比较。
在试验中，10项结果的分布各不相同，这表明在多种不良事件结果的背景下，采用实用的方法选择分布方法具有价值。在不同试验中存在三个结果，自动化程序针对相同结果选择的方法有所不同。分布方法识别出三个信号（嗜酸性粒细胞、血细胞比容和血红蛋白），而使用费舍尔精确检验时仅识别出一个信号（嗜酸性粒细胞），使用比例差异的95%置信区间识别出两个信号（嗜酸性粒细胞和钾）。
当连续不良事件结果的二分法有助于临床解释时，我们提倡使用分布方法来保留统计功效。现在方法易于实施。在RCT不良事件分析的背景下，保留信息尤为重要。这种自动化方法的常规实施需要进一步评估。
