Sheffield Centre for Health and Related Research (SCHARR), School of Medicine and Population Health, University of Sheffield, Sheffield, S1 4DA, UK.
Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.
BMC Med Res Methodol. 2024 Sep 27;24(1):216. doi: 10.1186/s12874-024-02339-7.
An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials.
We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed.
We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%).
Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved.
自适应设计允许根据累积数据修改设计，同时保持试验的有效性和完整性。在设计适应性试验时，最终样本量可能是未知的。因此，重要的是要考虑在研究计划中使用的样本量以及如何将其传达，以增加对试验设计的理解并促进稳健的计划。在本文中，我们回顾了关于随机适应性试验样本量报告的试验方案和资助申请。
我们在 ClinicalTrials.gov 上搜索了具有比较目标的随机试验方案（2010 年 1 月 1 日至 2022 年 12 月 31 日）。还包括从英国公共资助研究人员那里获得的当代合格资助申请。对适合的自适应设计记录进行了审查，并提取了关键信息并进行了描述性分析。
我们确定了 439 份记录，其中 265 项试验符合条件。其中，分别有 164 项（61.9%）和 101 项（38.1%）由行业和公共部门赞助，尽管使用的组序贯设计多种多样，但所有试验中有 169 项（63.8%）为组序贯设计。报告或直接推断的最大和最小样本量最多（n=199，75.1%）。方案中通常将假设不会触发适应性的样本量设置为估计的目标样本量。然而，在 152 项已完成的试验中，分别有 15 项（9.9%）和 33 项（21.7%）因试验适应性而增加或减少了样本量。在大多数情况下，样本量计算过程都得到了很好的报告（n=216，81.5%）；然而，有 116 项试验（43.8%）缺少样本量计算参数的合理性说明。不到一半的试验提供了足够的关于研究设计操作特征的信息（n=119，44.9%）。
尽管样本量的报告有所不同，但通常会报告最大和最小样本量。大多数试验是按照预计的入组人数进行计划的，假设不会触发适应性。尽管三分之一的报告试验改变了样本量。样本量计算通常得到很好的报告，但样本量计算参数的合理性说明和适应性设计的统计行为报告仍有待改进。
