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Streamlining Detection Strategies for Signal Detection

Posted on September 29th, 2017 by in Pharma R&D


Widespread underreporting is often cited as a factor that curtails the analytical value of data from spontaneous reporting systems. Voluntary submission of reports is a core feature of these systems but it also means that not all events suspected to be associated with a medicinal product are documented and brought to the attention of authorities and marketing authorization holders – this is especially true in cases where events are not considered severe. But there are ways to deal with underreporting by building several information sources into their surveillance mechanisms, such that spontaneous reports are just one of multiple touchpoints that reveal signals of possible adverse reactions.

A larger issue with detecting signals in data from spontaneous reporting is that while they represent an unrestricted view of the adverse events landscape, that landscape also happens to be very cluttered by data heterogeneity and biases. Some examples of that clutter include submission of several reports about a single event, incomplete information regarding an event or a patient’s medical history, a variable language used to describe events and medicines, or biases in submissions due to reporter behavior and knowledge. Also contributing to that clutter is a missing point of reference. The calculated risk of an adverse event associated with a drug has to mean only when the same risk can be calculated in the population not taking the drug. The latter is not represented in spontaneous reporting and therefore, selecting data that can serve as a meaningful reference is difficult and can dramatically impact outcomes.

Tapping into this cluttered landscape can generate signals that do not reflect reality, causing resource investment into validating an inaccuracy. At the same time, clutter can mask signals that are relevant to the comprehensive assessment of a medicinal product. Missing such signals can be detrimental to the marketability of the drug, not to mention dangerous for patients. So, how can signal detection be improved to maximize insights based on data from spontaneous adverse events reporting?

Read the white paper to learn more: Hypothesis-Driven Signal Detection: Success with Systematic Pharmacovigilance.

All opinions shared in this post are the author’s own.

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