Software for Near Real-Time Post-Market Drug and Vaccine Safety Surveillance

说明(申请人提供)：在一种新药或疫苗进入市场后，为了发现在第三阶段临床试验中太罕见而无法检测到的严重不良事件，上市后的安全性监测是重要的。这种监测传统上是基于自发的不良事件报告系统，但现在越来越多地使用来自健康保险计划的电子健康记录。如果出现重大安全问题，我们希望尽快了解情况。与疫苗安全数据链合作，我们率先使用了使用每周电子健康数据馈送和顺序统计分析的近实时药物和疫苗安全监测。为了实现这一点，开发了适用于上市后安全监测的新的序贯分析方法，其中优先考虑的是在大型观察数据中早期发现罕见的不良事件。在这个项目中，我们将改进现有的内部序贯统计分析软件，以便其他调查人员可以轻松使用。同样重要的是，我们将扩展该软件，以处理各种序贯研究设计的额外参数设置，包括自身对照分析、历史对照、同时对照和倾向得分匹配对照。该软件将用于数据分析和研究设计，考虑到总体类型1错误率、统计能力、当零假设被拒绝时发出信号的时间、当零假设没有被拒绝时的监测长度以及被监测的人口规模。该代码将以免费开源R包的形式发布，并附有高质量的用户指南。

DESCRIPTION (provided by applicant): After a new drug or vaccine enters the market, post-market safety surveillance is important in order to detect serious adverse events that are too rare to be detected during phase three clinical trials. Such surveillance has traditionally been based on spontaneous adverse event reporting systems but electronic health records from health insurance plans are now increasingly being used instead. If there is a major safety problem, we want to know about it as soon as possible. Working with the Vaccine Safety Datalink, we have pioneered the use of near real-time drug and vaccine safety surveillance using weekly electronic health data feeds and sequential statistical analysis. To accomplish this, new sequential analysis methods suitable for post- market safety surveillance were developed where the priority is on early detection of rare adverse events in large observational data. In thi project we will enhance existing in-house sequential statistical analysis software so that it can easily be used by other investigators. Equally important, we will expand the software to handle additional parameter settings for a wide variety of sequential study designs, including self-control analyses, historical controls, concurrent controls and propensity score matched controls. The software will be useful for data analysis as well as study design, considering overall type 1 error rates, statistical power, time to signal when the null hypothesis is rejected, the length of surveillance when the null is not rejected and the population size under surveillance. The code will be published as free open source R packages, with high quality user guides.