Better detection strategies in signal detection
Biases and heterogeneity in the data of spontaneous adverse event reporting systems can make it hard to gain the insights to inform decisions.
Capturing all possible indications of an adverse event may overcome these limitations, but involves extensive validation of multiple signals that may prove to be inaccurate.
To streamline these processes, detection strategies can be constructed to probe a more defined association between an event and a drug. This hypothesis-driven approach builds on multi-sourced information and requires an information framework that unifies that information and aligns signal detection and validation.
In this white paper, you will learn more about how to make the most of invaluable data by:
- Implementing multiple hypothesis-driven strategies
- Calculating a clearly defined background
- Building details into detection strategies to identify confounding factors