PDF(Pubmed)
Abstract：
Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.
摘要：
大多数肿瘤学试验根据频率显著性阈值定义了实验治疗与对照治疗相比的优越性。被广泛误解。通过贝叶斯推断计算的后验概率分布可能是更直观的不确定性度量，特别是对于临床益处的测量，例如最小临床重要差异（MCID）。这里,我们手动重建了230个III期的194,129个患者水平的结果，优越性设计，肿瘤学试验。后验是通过使用标准先验的马尔可夫链蒙特卡罗抽样计算的。所有被解释为阳性的试验的边际效益概率>90%(HR<1)。然而,38％的阳性试验达到MCID的概率≤90％（HR<0.8），即使在热情的事先。对82项获得监管部门批准的试验进行的亚组分析显示，在热情的先验下，30%的人符合MCID的概率≤90%。相反，24%的阴性试验有>90%的概率实现边际效益，即使在怀疑之前，包括12项主要终点为总生存期的试验。最后,来自先前工作的III期肿瘤学特异性之前，它使用公布的汇总统计数据而不是重建的数据来计算后验，验证了个体患者水平的数据发现。一起来看,这些结果表明,贝叶斯模型为III期肿瘤学试验增加了相当独特的解释价值,并为克服驳斥零假设与获得MCID之间的差异提供了可靠的解决方案.
结论：肿瘤学试验的统计分析通常通过计算P值来进行，虽然人们对这些知之甚少。使用P值截止值，如P<0.05，可能导致一些治疗被接受，没有什么好处，和其他疗法被拒绝，有相当大的好处。可以通过贝叶斯统计来计算更直观和直接的概率-实验性治疗优于标准治疗。在这里，我们使用软件获得了230项试验中纳入的194,129名患者的结果，然后计算受益概率。基于P值的解释与三分之一试验的获益概率不一致。这项研究表明，受益概率将大大提高肿瘤学试验的解释。
结论：肿瘤学试验的统计分析通常通过计算P值来进行，虽然人们对这些知之甚少。使用P值截止值，如P<0.05，可能导致一些治疗被接受，没有什么好处，和其他疗法被拒绝，有相当大的好处。可以通过贝叶斯统计来计算更直观和直接的概率-实验性治疗优于标准治疗。在这里，我们使用软件获得了230项试验中纳入的194,129名患者的结果，然后计算受益概率。基于P值的解释与三分之一试验的获益概率不一致。这项研究表明，受益概率将大大提高肿瘤学试验的解释。
