University of Florida, Gainesville, Florida, USA.
Florida State University, Tallahassee, Florida, USA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:717-726. eCollection 2020.
Low trial generalizability is a concern. The Food and Drug Administration had guidance on broadening trial eligibility criteria to enroll underrepresented populations. However, investigators are hesitant to do so because of concerns over patient safety. There is a lack of methods to rationalize criteria design. In this study, we used data from a large research network to assess how adjustments of eligibility criteria can jointly affect generalizability and patient safety (i.e the number of serious adverse events [SAEs]). We first built a model to predict the number of SAEs. Then, leveraging an a priori generalizability assessment algorithm, we assessed the changes in the number of predicted SAEs and the generalizability score, simulating the process of dropping exclusion criteria and increasing the upper limit of continuous eligibility criteria. We argued that broadening of eligibility criteria should balance between potential increases of SAEs and generalizability using donepezil trials as a case study.
低试验推广性是一个关注点。食品和药物管理局曾有指导意见，放宽试验纳入标准，以纳入代表性不足的人群。然而，由于对患者安全的担忧，调查人员对此犹豫不决。缺乏合理的标准设计方法。在这项研究中，我们使用来自大型研究网络的数据来评估纳入标准的调整如何共同影响推广性和患者安全（即严重不良事件的数量）。我们首先建立了一个预测严重不良事件数量的模型。然后，利用一个事先的推广性评估算法，我们评估了预测严重不良事件数量和推广性得分的变化，模拟了排除标准的放宽和连续纳入标准上限的提高过程。我们认为，使用多奈哌齐试验作为案例研究，在潜在严重不良事件增加和推广性之间，纳入标准应该进行平衡。
