Sandrock Christian E, Song Peter X K
Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, UC Davis Medical Center, University of California, Davis, 2315 Stockton Blvd, Sacramento, CA, 95817, USA.
School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI, USA.
Trials. 2024 Dec 18;25(1):822. doi: 10.1186/s13063-024-08679-5.
Current guidelines tend to focus on a p-value threshold of a pre-specified primary endpoint tested in randomized controlled clinical trials to determine a treatment effect for a specific drug. However, a p-value does not always provide evidence on the treatment effect of a drug, especially when stratification of the data does not account for unforeseen variables introduced into the analysis. We report and discuss a rare case in which investigational site stratification in the pre-specified analysis method of a primary endpoint results in a loss of statistical power in the evaluation of the treatment effect due to data attrition of almost 17% of outcome data in the phase III randomized, controlled PANAMO study in critically ill COVID-19 patients. Other analyses utilizing no or different stratification (e.g., stratifying by country, region, pooling low enrollment clinical sites) evaluates 100% of patient data resulting in p-values suggesting a positive treatment effect (p < 0.05). We demonstrate how this technical artifact occurs by adjustment for site stratification within the Cox regression analysis for survival outcomes and how alternative stratification corrects this discrepancy.
当前指南倾向于关注在随机对照临床试验中针对预先指定的主要终点所测试的p值阈值，以确定特定药物的治疗效果。然而，p值并不总是能提供有关药物治疗效果的证据，尤其是当数据分层未考虑到分析中引入的意外变量时。我们报告并讨论了一个罕见案例，在一项针对重症COVID-19患者的III期随机对照PANAMO研究中，主要终点预先指定的分析方法中的研究地点分层，由于近17%的结局数据出现数据损耗，导致在评估治疗效果时统计效力丧失。其他未进行分层或采用不同分层方法的分析（例如，按国家、地区分层，合并入组人数少的临床地点）评估了100%的患者数据，得出的p值表明治疗效果为阳性（p < 0.05）。我们通过在生存结局的Cox回归分析中对地点分层进行调整，展示了这种技术假象是如何产生的，以及替代分层如何纠正这种差异。
