Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Eur J Cancer. 2023 Nov;194:113357. doi: 10.1016/j.ejca.2023.113357. Epub 2023 Sep 22.
The 'Table 1 Fallacy' refers to the unsound use of significance testing for comparing the distributions of baseline variables between randomised groups to draw erroneous conclusions about balance or imbalance. We performed a cross-sectional study of the Table 1 Fallacy in phase III oncology trials.
From ClinicalTrials.gov, 1877 randomised trials were screened. Multivariable logistic regressions evaluated predictors of the Table 1 Fallacy.
A total of 765 randomised controlled trials involving 553,405 patients were analysed. The Table 1 Fallacy was observed in 25% of trials (188 of 765), with 3% of comparisons deemed significant (59 of 2353), approximating the typical 5% type I error assertion probability. Application of trial-level multiplicity corrections reduced the rate of significant findings to 0.3% (six of 2345 tests). Factors associated with lower odds of the Table 1 Fallacy included industry sponsorship (adjusted odds ratio [aOR] 0.29, 95% confidence interval [CI] 0.18-0.47; multiplicity-corrected P < 0.0001), larger trial size (≥795 versus <280 patients; aOR 0.32, 95% CI 0.19-0.53; multiplicity-corrected P = 0.0008), and publication in a European versus American journal (aOR 0.06, 95% CI 0.03-0.13; multiplicity-corrected P < 0.0001).
This study highlights the persistence of the Table 1 Fallacy in contemporary oncology randomised controlled trials, with one of every four trials testing for baseline differences after randomisation. Significance testing is a suboptimal method for identifying unsound randomisation procedures and may encourage misleading inferences. Journal-level enforcement is a possible strategy to help mitigate this fallacy.
“表 1 谬误”是指在比较随机分组之间的基线变量分布时，使用错误的显著性检验来得出关于平衡或不平衡的错误结论。我们对 III 期肿瘤学试验中的表 1 谬误进行了横断面研究。
从 ClinicalTrials.gov 筛选了 1877 项随机试验。多变量逻辑回归评估了表 1 谬误的预测因素。
共分析了 765 项涉及 553405 名患者的随机对照试验。25%的试验（188/765）观察到表 1 谬误，3%的比较被认为有统计学意义（2353 次比较中的 59 次），接近典型的 5%Ⅰ类错误断言概率。应用试验水平的多重性校正将有统计学意义的发现率降低至 0.3%（2345 次检验中的 6 次）。与表 1 谬误发生几率较低相关的因素包括产业资助（校正比值比[aOR]0.29，95%置信区间[CI]0.18-0.47；多重校正 P<0.0001）、试验规模较大（≥795 例患者与<280 例患者；aOR 0.32，95%CI 0.19-0.53；多重校正 P=0.0008）和发表在欧洲杂志与美国杂志（aOR 0.06，95%CI 0.03-0.13；多重校正 P<0.0001）。
本研究强调了表 1 谬误在当代肿瘤学随机对照试验中的持续存在，每四个试验中就有一个在随机化后测试基线差异。显著性检验是识别不合理随机化程序的次优方法，可能会鼓励误导性推断。期刊层面的执行可能是减轻这种谬误的一种策略。
