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

描述（由申请人提供）：在新药或疫苗进入市场后，上市后安全性监测非常重要，以便发现在三期临床试验期间因过于罕见而无法发现的严重不良事件。这种监测传统上是基于自发不良事件报告系统，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。如果有重大的安全问题，我们想要尽快知道。我们与疫苗安全数据链合作，率先使用每周电子卫生数据馈送和顺序统计分析，进行近乎实时的药物和疫苗安全监测。为了实现这一目标，新的序列分析方法适用于上市后安全监测，重点是在大量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顺序统计分析软件，以便其他调查人员可以轻松使用。同样重要的是，我们将扩展软件以处理各种顺序研究设计的附加参数设置，包括自我控制分析，历史对照，并发对照和倾向评分匹配对照。该软件将对数据分析和研究设计有用，考虑到总体1型错误率，统计能力，零假设被拒绝时的信号时间，零假设未被拒绝时的监测长度以及监测下的人口规模。代码将作为免费的开源R包发布，并提供高质量的用户指南。

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.