Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance

Abstract

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.

An evaluation of statistical approaches to postmarketing surveillance

“…Continual monitoring of drugs and therapeutic biologics after approval for use is essential for patient and consumer safety for many years after approval . Administrative claims and electronic health record data are often used for epidemiological studies postlicensure . Analysis of safety data from spontaneous reporting systems (SRS), such as the FDA Adverse Event Reporting System (FAERS), remains a mainstay for monitoring the safety of these products and identification of safety signals .…”

“…7 Administrative claims and electronic health record data are often used for epidemiological studies postlicensure. [8][9][10][11] Analysis of safety data from spontaneous reporting systems (SRS), such as the FDA Adverse Event Reporting System (FAERS), remains a mainstay for monitoring the safety of these products and identification of safety signals. 12,13 Individual case safety reports (ICSRs) contained in such systems provide first alerts for emerging drug and therapeutic biologic product safety concerns and help to generate hypotheses that can be evaluated using other data sources and methods.…”

Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

“…Five PV articles were selected for breadth of methodology and potential applicability to medical devices and therefore should not be interpreted as the current state of PV practice. PV methods groups include Disproportionality , Propensity Score , Risk Adjustment , Bayesian , Sequential Probability Ratio , and Advanced Data Mining . Among the 14 reviewed Device articles, methods groups ordered by number of articles were Disproportionality , Risk Adjustment , Sequential Probability Ratio , Survival Modeling , Propensity Score , Bayesian, and Advanced Data Mining .…”

Sources of Safety Data and Statistical Strategies for Design and Analysis: Clinical Trials

“…Data from real-world sources and PV databases often reflect greater diversity and are more representative of patient experience. 4,18,128,129 While clinical trials provide high-quality data and an initial assessment of the safety profile of a new therapy, they cannot fully characterize the safety profile on their own. Safety signals are often quite small due to the above-mentioned reasons.…”

“…Thus, postmarket observational methods and real-world data sources play a critical role in further improving the safety profile of a drug. 4,18,[128][129][130]…”