Baldi Ileana, Gregori Dario, Desideri Alessandro, Berchialla Paola
Department of Cardiac, Thoracic and Vascular Sciences, Unit of Biostatistics, Epidemiology and Public Health, University of Padova, Padova, Italy.
Cardiovascular Research Foundation, San Giacomo Hospital, Castelfranco Veneto, Italy.
Open Heart. 2017 Dec 17;4(2):e000720. doi: 10.1136/openhrt-2017-000720. eCollection 2017.
To provide brief guidance on how to design accrual monitoring activities in a clinical trial protocol.
Two completed clinical trials that did not achieve the planned sample size, the Cost of Strategies After Myocardial Infarction (COSTAMI) trial and the Biventricular Pacing After Cardiac Surgery (BiPACS) trial.
A Bayesian monitoring tool, the constant accrual model, is applied retrospectively to accrual data from each case study to illustrate how the tool could be used to identify problems with accrual early in the trial period and to frame the conditions in which the approach can be used in practice.
After 312 days and 155 patients enrolled in the COSTAMI trial, accrual could be classified as 'off target' on the basis of statistical criteria outlined in the protocol. As for the BiPACS trial, after 2 years, it was already evident that the accrual was 'considerably off target'.
Prompt awareness of a high risk of accrual failure could trigger different interventions to overcome protocol-related, patient-related or investigator-related barriers to recruitment or ultimately contribute to an early stopping decision due to recruitment futility.Accrual prediction models should be included as standard tools for routine monitoring activities in cardiovascular research. Among them, methods relying on the Bayesian approach are particularly attractive, as they can naturally update past evidence when actual accrual data becomes available.
为如何在临床试验方案中设计入组监测活动提供简要指导。
两项已完成但未达到计划样本量的临床试验,即心肌梗死后策略成本(COSTAMI)试验和心脏手术后双心室起搏(BiPACS)试验。
一种贝叶斯监测工具,即恒定入组模型,被回顾性应用于每个案例研究的入组数据,以说明该工具如何用于在试验早期识别入组问题,并阐述该方法在实际中可使用的条件。
在COSTAMI试验中,入组312天且纳入155例患者后,根据方案中概述的统计标准,入组可被归类为“偏离目标”。至于BiPACS试验,在2年后,入组“明显偏离目标”已经很明显。
对入组失败的高风险的及时认识可能会引发不同的干预措施,以克服与方案、患者或研究者相关的招募障碍,或者最终由于招募无效而促成提前终止试验的决定。入组预测模型应作为心血管研究常规监测活动的标准工具纳入。其中,依赖贝叶斯方法的方法特别有吸引力,因为当实际入组数据可用时,它们可以自然地更新过去的证据。