Signal Detection and Monitoring Based on Longitudinal Healthcare Data

• Main Authors: Iris Pigeot, Marc Suling
• Format: Article
• Language: English
• Published: MDPI AG 2012-12-01
• Series: Pharmaceutics
• Subjects: bayesian signal detection; confounder adjustment; disproportionality analysis; longitudinal data; pharmacovigilance; signal detection; spontaneous reporting; surveillance techniques
• Online Access: http://www.mdpi.com/1999-4923/4/4/607

Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of the signal detection process and introduces some types of data sources typically used. The most commonly applied signal detection algorithms are presented, covering simple frequentistic methods like the proportional reporting rate or the reporting odds ratio, more advanced Bayesian techniques for spontaneous and longitudinal data, e.g., the Bayesian Confidence Propagation Neural Network or the Multi-item Gamma-Poisson Shrinker and methods developed for longitudinal data only, like the IC temporal pattern detection. Additionally, the problem of adjustment for underlying confounding is discussed and the most common strategies to automatically identify false-positive signals are addressed. A drug monitoring technique based on Wald’s sequential probability ratio test is presented. For each method, a real-life application is given, and a wide set of literature for further reading is referenced.