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The role of electronic tracking in monitoring data output in clinical trials
Published in Frank Wells, Michael Farthing, Fraud and Misconduct in Biomedical Research, 2019
Erick Gaussens, Pierre-Henri Bertoy, Jean-Marc Husson
This chapter will focus on the use of electronic tracking to detect and prevent scientific misconduct in the field of health products, mainly medicinal products. The European Union guidelines on pharmacovigilance5 highlight data mining techniques for searching signals in pharmacovigilance databases, including Proportional Reporting Ratio (PRR), Bayesian, chi-square, log-likelihood, etc.
Changing paradigms in detecting rare adverse drug reactions: from disproportionality analysis, old and new, to machine learning
Published in Expert Opinion on Drug Safety, 2022
Daniel Arku, Consuela Yousef, Ivo Abraham
For its time (the early 1980s), the methodology of disproportionality analysis was rather ingenious. To quote Faillie [9], its aim is to determine whether there is a ‘higher than expected number of adverse reaction reports with a specific drug’ and whether the reporting rate of an ADR is ‘disproportionate’ relative ‘to other reactions in the pharmacovigilance database.’ In other words: does the observed rate of an ‘ADR-of-interest’ exceed, and by how much, its expected rate. Expected rate refers to the rate when both the ADR and the drug-of-interest are not associated but have been observed concurrently (for instance: an adverse event observed in a clinical trial but not attributed to the drugs being evaluated? The frequentist statistics were commensurate with the time: construct a 2 × 2 table; calculate the expected cell values and the difference between observed and expected values (O/E estimate); and subject it to a χ2 (chi-squared) test of independence (a case of the gamma distribution). In the process, estimate the proportional reporting ratio (PRR) and its confidence interval. Of note, the Uppsala Monitoring Center, which manages Vigibase on behalf of the WHO, has developed triage algorithms for the early discovery and identification of ADRs [10,11].
Performance of subgrouped proportional reporting ratios in the US Food and Drug Administration (FDA) adverse event reporting system
Published in Expert Opinion on Drug Safety, 2023
Daniel G. Dauner, Rui Zhang, Terrence J. Adam, Eleazar Leal, Viviene Heitlage, Joel F. Farley
Signal detection algorithms (SDA) are used to identify signals in adverse drug event (ADE) databases. Many SDAs give the same weight to information from all reports regardless of products and patients, which may result in signals being masked or false positives being flagged as potential signals [1,2]. Subgrouped analysis, which is generally used in epidemiology to reduce confounding and highlight effect modification, can be used to help correct for this [3]. The proportional reporting ratio (PRR) is a disproportionality measure that calculates the proportion of a specified ADE for a drug of interest and compares it to the proportion of the same ADE in all other drugs in a database [4]. However, few studies have used subgroup analysis with the PRR [5].