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.

Public Health Relevance

Huge observational electronic health data sets are available for drug and vaccine safety surveillance, and there is a greatly increasing interest in using them for post-market near real-time safety surveillance to quickly detect rare but serious adverse events. For this purpose, we have developed sequential statistical methods and simple computer programs. This project will enhance the software so that it can be used by others in a user friendly manner, and it will expand the software so that it can be used for a much wider range of populations, drugs, vaccines and adverse event outcomes.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Marcus, Stephen
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Brigham and Women's Hospital
United States
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