Benchmarks for detecting 'breakthroughs' in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation.
Benchmarks for detecting 'breakthroughs' in clinical trials: empirical assessment of the probability of large treatment effects using kernel density estimation.
复制标题
检测临床试验中“突破”的基准：使用核密度估计对大治疗效果的概率进行实证评估。
DOI：
10.1136/bmjopen-2014-005249
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发表时间：
2014
期刊：
影响因子：
2.9
通讯作者：
Djulbegovic,Benjamin
中科院分区：
文献类型：
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作者：
Miladinovic,Branko;Kumar,Ambuj;Mhaskar,Rahul;Djulbegovic,Benjamin
目的了解“突破”，即显著改善健康结果的治疗方法的开发频率。我们应用加权自适应核密度估计来构建概率密度函数，用于从五个公共资助队列和一个私人资助组中观察到的治疗效果。数据来源由五个公共资助的合作小组进行了820项试验，涉及1064项比较，纳入331004名患者。葛兰素史克（GlaxoSmithKline）进行了40项癌症试验，涉及50项比较，总共招募了19889名患者。结果我们计算出检测到效果大的治疗的概率为10%(5-25%)，检测到效果非常大的治疗的概率为2%（0.3-10%）。研究人员自己判断，他们在16%的试验中发现了一种新的、突破性的干预措施。我们建议这些数字作为衡量未来“突破性”治疗发展的基准。
ObjectiveTo understand how often ‘breakthroughs,’ that is, treatments that significantly improve health outcomes, can be developed.DesignWe applied weighted adaptive kernel density estimation to construct the probability density function for observed treatment effects from five publicly funded cohorts and one privately funded group.Data Sources820 trials involving 1064 comparisons and enrolling 331 004 patients were conducted by five publicly funded cooperative groups. 40 cancer trials involving 50 comparisons and enrolling a total of 19 889 patients were conducted by GlaxoSmithKline.ResultsWe calculated that the probability of detecting treatment with large effects is 10% (5–25%), and that the probability of detecting treatment with very large treatment effects is 2% (0.3–10%). Researchers themselves judged that they discovered a new, breakthrough intervention in 16% of trials.ConclusionsWe propose these figures as the benchmarks against which future development of ‘breakthrough’ treatments should be measured.