今天,来自多个来源的大数据的激增正在增加药物警戒必须赢得的赌注,使证据综合成为该领域越来越稳健的方法。在这种情况下,许多学者认为,数据挖掘产生的新的计算方法将有效地增强对药物不良反应预警信号的检测,解决上市后监控所需的挑战。本文强调了需要一种哲学方法才能充分实现药物警戒2.0革命。介绍了证据综合的最新技术,其次是电子合成的说明,因果评估的贝叶斯框架。有关剂量反应证据的计算结果在本文末尾显示。
Today\'s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article.