Medical Statistics Group, School of Health and Related Research, University of Sheffield, 30 Regent Court, Regent Street, Sheffield S1 4DA, UK.
Trials. 2014 Feb 17;15:61. doi: 10.1186/1745-6215-15-61.
Publicly funded trials regularly fail to recruit their target sample size or find a significant positive result. Adaptive clinical trials which may partly mediate against the problems are not often applied. In this paper we investigate the potential of a form of adaption in a clinical trial - a futility analysis - to see if it has potential to improve publicly funded trials.
Outcome data from trials funded by two UK bodies, the Health Technology Assessment (HTA) programme and the UK Medical Research Council (MRC), were collected. These data were then used to simulate each trial with a single futility analysis using conditional power, undertaken after 50% to 90% of the patients had been recruited. Thirty-three trials recruiting between 2002 and 2008 met the inclusion criteria. Stopping boundaries of conditional powers of 20%, 30% and 40% were considered and outcomes included the number of trials successfully stopped and number of patients saved.
Inclusion of a futility analysis after 75% of the patients had been recruited would have potentially resulted in 10 trials, which went on to have negative results, correctly stopping for futility using a stopping boundary of 30%. A total of 807 patients across all the trials would potentially have been saved using these futility parameters. The proportion of studies successfully recruiting would also have increased from 45% to 64%.
A futility assessment has the potential to increase efficiency, save patients and decrease costs in publicly funded trials. While there are logistical issues in undertaking futility assessments we recommend that investigators should aim to include a futility analysis in their trial design wherever possible.
公共资助的试验经常未能招募到目标样本量，或发现显著的阳性结果。适应性临床试验可能部分缓解这些问题，但并不经常应用。本文旨在探讨临床试验中一种适应形式（即无效性分析）的潜力，以了解其是否有可能改善公共资助试验。
收集了两个英国机构（卫生技术评估计划和英国医学研究理事会）资助的试验的结局数据。然后，使用条件功效对这些数据进行模拟，在 50%至 90%的患者入组后，对每个试验进行一次无效性分析。共有 33 项招募时间在 2002 年至 2008 年间的试验符合纳入标准。考虑了条件功效的停止边界为 20%、30%和 40%，结局包括成功停止试验的数量和节省的患者数量。
在 75%的患者入组后纳入无效性分析，可能会使 10 项试验因无效而正确停止，使用 30%的停止边界。通过这些无效性参数，所有试验中可能会有 807 名患者得到挽救。成功招募研究的比例也会从 45%增加到 64%。
无效性评估有可能提高公共资助试验的效率，节省患者并降低成本。虽然进行无效性评估存在后勤问题，但我们建议研究人员应尽可能在试验设计中纳入无效性分析。
