Heath Anna, Offringa Martin, Pechlivanoglou Petros, Rios Juan David, Klassen Terry P, Poonai Naveen, Pullenayegum Eleanor
Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada.
Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada.
Contemp Clin Trials Commun. 2020 Apr 8;18:100561. doi: 10.1016/j.conctc.2020.100561. eCollection 2020 Jun.
BACKGROUND/AIMS: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the "inferior" intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials.
We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes.
We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%.
Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims.
ClinicalTrials.gov NCT02828566 July 11, 2016.
背景/目的：非劣效性试验旨在研究一种新型干预措施（通常具有其他优势，如成本更低或更安全）是否与现有治疗方法具有相似的临床疗效。在非劣效性试验的中期证据表明新型治疗方法确实较差的情况下，继续将患者随机分配至“较差”干预措施会引发伦理问题。因此，如果中期数据表明在试验结束时得出非劣效性结论的可能性不大，则应考虑因无效而停止试验。迄今为止，可用于指导非劣效性试验停止规则制定的实例有限。
我们采用贝叶斯预测效能方法，为收集二元结局的试验制定了因无效而停止试验的规则。我们评估了该停止规则的频率特性，以确保对I型和II型错误的控制。我们的案例研究是鼻内氯胺酮用于程序性镇静试验（INK试验），这是一项非劣效性试验旨在评估通过两种替代途径给药的氯胺酮的镇静特性。
我们考虑在INK试验纳入560名患者中的140名患者后实施我们的停止规则。如果与标准治疗相比，新型治疗方法多出现12例失败病例，则试验将停止。该试验的I型错误率为2.2%，效能为80%。
在非劣效性试验中因无效而停止试验可减少无效治疗的暴露，并为其他研究问题保留资源。基于贝叶斯预测效能的无效性停止规则易于实施且与试验目标一致。
ClinicalTrials.gov NCT02828566，2016年7月11日。
