Statistical Perspectives on Drug Safety Screening using Spontaneous Reporting Data and Electronic Health Records

Screening spontaneous reporting (SR) or electronic health records (EHR) data sets for adverse drug reactions (ADRs) has become an important component of drug safety. Much methodological work has been historically done on SR data, however, novel approaches are continually being suggested which merit critical review. Thus, we will first investigate some of these approaches as well as consider the issues of drug-drug interactions in SR data. Next, we will address the analysis of EHR data from two perspectives. First, standard approaches to analyze time-to-event data can be employed; however, misclassification of the study variable (misdiagnosis) can shift ADR risk estimates. We will use an internal validation sampling approach to derive adjusted methods of estimation to minimize this effect. Second, we propose to model EHR data from a multiple failure with competing risks framework. All approaches will be investigated through the use of large scale simulations. Finally, Risk Sciences International (RSI) will benefit from the development of a breadth of algorithms that can be used to search from potential problematic drugs in both types of data. Screening these data sets can be marketed to regulatory businesses in government or the private sector.

Faculty Supervisor:

Dr. Patrick J. Farrell

Student:

Christopher Gravel

Partner:

Risk Sciences International

Discipline:

Mathematics

Sector:

Pharmaceuticals

University:

Carleton University