Sauzet Odile, Cornelius Victoria
Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany.
Department of Epidemiology and International Public Health, Bielefeld School of Public Health (BiSPH), Bielefeld University, Bielefeld, Germany.
Front Pharmacol. 2022 Aug 23;13:889088. doi: 10.3389/fphar.2022.889088. eCollection 2022.
Pharmacovigilance is the process of monitoring the emergence of harm from a medicine once it has been licensed and is in use. The aim is to identify new adverse drug reactions (ADRs) or changes in frequency of known ADRs. The last decade has seen increased interest for the use of electronic health records (EHRs) in pharmacovigilance. The causal mechanism of an ADR will often result in the occurrence being time dependent. We propose identifying signals for ADRs based on detecting a variation in hazard of an event using a time-to-event approach. Cornelius et al. proposed a method based on the Weibull Shape Parameter (WSP) and demonstrated this to have optimal performance for ADRs occurring shortly after taking treatment or delayed ADRs, and introduced censoring at varying time points to increase performance for intermediate ADRs. We now propose two new approaches which combined perform equally well across all time periods. The performance of this new approach is illustrated through an EHR Bisphosphonates dataset and a simulation study. One new approach is based on the power generalised Weibull distribution (pWSP) introduced by Bagdonavicius and Nikulin alongside an extended version of the WSP test, which includes one censored dataset resulting in improved detection across time period (dWSP). In the Bisphosphonates example, the pWSP and dWSP tests correctly signalled two known ADRs, and signal one adverse event for which no evidence of association with the drug exist. A combined test involving both pWSP and dWSP is reliable independently of the time of occurrence of ADRs.
药物警戒是指对已获许可并正在使用的药物所产生的危害进行监测的过程。其目的是识别新的药物不良反应（ADR）或已知ADR发生率的变化。在过去十年中，人们对在药物警戒中使用电子健康记录（EHR）的兴趣日益增加。ADR的因果机制通常会导致其发生具有时间依赖性。我们建议使用事件发生时间方法，通过检测事件风险的变化来识别ADR信号。科尼利厄斯等人提出了一种基于威布尔形状参数（WSP）的方法，并证明该方法对于治疗后不久发生的ADR或延迟性ADR具有最佳性能，同时引入了不同时间点的删失以提高对中间性ADR的检测性能。我们现在提出两种新方法，它们在所有时间段的综合性能相同。通过一个EHR双膦酸盐数据集和一项模拟研究来说明这种新方法的性能。一种新方法基于巴格多纳维丘斯和尼古林提出的幂广义威布尔分布（pWSP）以及WSP检验的扩展版本，其中包括一个删失数据集，从而在整个时间段内实现了更好的检测（dWSP）。在双膦酸盐的例子中，pWSP和dWSP检验正确地发出了两种已知ADR的信号，并发出了一种与该药物无关联证据的不良事件信号。涉及pWSP和dWSP的联合检验无论ADR的发生时间如何都是可靠的。
