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

用于近实时上市后药物和疫苗安全监测的软件

基本信息

批准号：
8927656
负责人：
Martin Kulldorff
金额：
$ 28.99万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8927656
关键词：
Adverse event Calendar Centers for Disease Control and Prevention (U.S.)Clinical Trials Code Communities Computer software Data Data Analyses Data Set Databases Development Documentation Early Diagnosis Educational process of instructing Educational workshop Electronic Health Record Electronics Enrollment Feeds Frequencies Health Health Insurance Health Planning Housing Lead Learning Length Maintenance Marketing Medical Methods Modeling Monitor Outcome Pattern Pharmaceutical Preparations Phase Population Population Sizes Programming Languages Publishing Reporting Research Design Research Personnel Safety Self-control as a personality trait Sensitivity and Specificity Serious Adverse Event Signal Transduction Statistical Computing Statistical Methods System Techniques Testing Time Training Translating Vaccines Work Writing base computer program disorder prevention health data interest open source post-market programs response user-friendly vaccine safety

项目摘要

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.

描述（由申请人提供）：在新药或疫苗进入市场之后，市场安全性监视对于检测严重的不良事件很重要，而在第三阶段临床试验期间很少检测到的严重不良事件。传统上，这种监视是基于自发性不利事件报告系统的基础，但是现在越来越多地使用了健康保险计划的电子健康记录。如果存在主要的安全问题，我们想尽快知道。使用疫苗安全数据链接，我们开创了使用近实时药物和疫苗安全监视的使用，并使用每周的电子健康数据提要和顺序统计分析。为此，开发了适合于市场安全后监视的新的顺序分析方法，在大型观察数据中，优先级是对罕见不良事件的优先级。在THI项目中，我们将增强现有的内部顺序统计分析软件，以便其他研究人员可以轻松使用它。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其他参数设置，包括自我控制分析，历史控制，并发控制和倾向得分匹配的控件。该软件将用于数据分析和研究设计，考虑到总1型错误率，统计功率，何时拒绝零假设的信号，何时未拒绝无效的监视时间以及监视下的人口大小。该代码将作为免费的开源R软件包发布，并具有高质量的用户指南。